Information Fusion and Geographic Information Systems: Proceedings of the Fourth International Workshop, 17-20 May 2009 (Lecture Notes in Geoinformation and Cartography)

Lecture Notes in Geoinformation and Cartography Series Editors: William Cartwright, Georg Gartner, Liqiu Meng, Michael ...

Author: Vasily V. Popovich | Manfred Schrenk | Christophe Claramunt | Kyrill V. Korolenko

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Lecture Notes in Geoinformation and Cartography Series Editors: William Cartwright, Georg Gartner, Liqiu Meng, Michael P. Peterson

Vasily V. Popovich · Manfred Schrenk · Christophe Claramunt · Kyrill V. Korolenko (Eds.)

Information Fusion and Geographic Information Systems Proceedings of the Fourth International Workshop, 17–20 May 2009

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Editors Vasily V. Popovich Deputy Director of SPIIRAS for Research, Professor, Dr Sci Tech 39, 14th Linia, V.O. 199178 St. Petersburg Russia [email protected]

Christophe Claramunt Naval Academy Research Institute Lanveoc-Poulmic BP 600 F-29240 Brest Naval France [email protected]

Manfred Schrenk CEIT ALANOVA gemeinnützige GmbH Central European Institute of Technology Department for Urbanism, Transport, Environment and Information Society Am Concorde Park 2, Gebäude F A-2320 Schwechat Austria [email protected]

Kyrill V. Korolenko, P.E. Chief Scientist/NUWC Code 1543, B1320 1176 Howell St. Newport RI 02841-1708 USA [email protected]

ISSN 1863-2246 ISBN 978-3-642-00303-5 DOI 10.1007/978-3-642-00304-2

e-ISBN 978-3-642-00304-2

Springer Dordrecht Heidelberg London New York Library of Congress Control Number: 2009922217 c Springer-Verlag Berlin Heidelberg 2009 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover design: deblik, Berlin Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Preface

This volume contains the papers presented at the International Workshop “Information Fusion and Geographic Information Systems” (IF&GIS’09) held in St. Petersburg, Russia in May 2009. The workshop was organized by the St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS). The workshop continues a series organised biannually, and attracts academics and industrials from a wide range of disciplines including computer science, geography, statistics, mathematics, hydrography, geomorphology, and environmental sciences. The objective of this workshop is to provide a forum for innovative research oriented towards Geographic Information Science and technologies and Corporate Information Systems whose close association highlight novel theoretical and practical challenges. The papers selected by the International Program Committee cover a wide range of innovative areas including ontological and semantic approaches for the representation of geographical data, geographical data monitoring, situation management and forecast, to emerging applications oriented to the maritime environment, disaster management and security threats. While traditional topics of GIS conferences are well represented and still being advanced, several new domains appear and stress the need for the development of versatile monitoring systems and decision making systems. While GIS already have a de facto standard for geographical monitoring and analysis, the papers accepted in this volume also illustrate several novel directions of application whose objective is more closely oriented to process modeling and decision making, and where the nature of the objects represented is revisited using ontological and semantic approaches. One of the emerging application that is particularly represented is the one of security threats. Since the devastating 9/11 attacks in 2001, monitoring security threats has become one of the most important challenge to international governments. It is then vitally important to monitor terrorist threats using novel mathematical and GIS methods in order to find appropriate scientific approaches to prevent terrorist acts. Maritime GIS are also at the leading edge of the novel applications oriented to navigation safety and are also addressed by several contributions presented at the workshop. The issues of information assurance and protection for GIS have been also integrated in the workshop program, v

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Preface

including computer system protection, complex security maintenance of information technologies, these being applicable to GIS and whose impact should be surely larger. The submission process attracted 41 abstracts from 14 countries, from which 35 papers were selected for submission. After a thorough reviewing process the International Program Committee accepted 25 papers from 8 countries for publication, including two invited papers. The accepted papers were allocated to the following six sessions: Corporate Information Systems and GIS; GIS as a Basis for Monitoring Systems; Ontology and Programming Technologies for GIS and GIS Applications; Monitoring of Terrorist Threat Based on GIS; Maritime GIS; Information Assurance and Protection in GIS. The IF&GIS’09 program was enriched by contributions from two distinguished invited speakers: Gennady Andrienko from the Fraunhofer Institute in Germany, and Victor Lobo from the Portuguese Naval Academy whose papers are also included in the volume. The success of the workshop was assured by the team efforts of sponsors, organizers, reviewers and participants. We would like to acknowledge the contribution of the International Program Committee members and thank all reviewers for their support and hard work. Our sincere gratitude goes out to all participants and all the authors of submitted papers. We are grateful to our sponsors, the Russian Academy of Sciences and the US Office of Naval Research Global (ONRGlobal) for their generous support. Finally, we wish to express our gratitude to Springer’s LNCS team, managed by Dr. Christian Witschel for their efficacious help and collaboration. May 2009

Vasily Popovich Manfred Schrenk Christophe Claramunt Kyrill Korolenko

Table of Contents

Preface ........................................................................................... v Organization ................................................................................. xi Invited Papers Visual Analytics for Geographic Analysis, Exemplified by Different Types of Movement Data G. Andrienko, N. Andrienko ................................................................... 3 Application of Self-Organizing Maps to the Maritime Environment Victor J.A.S. Lobo................................................................................. 19 CIS and GIS Concept for Corporate Information Systems Development Based on GIS Technologies V. Popovich........................................................................................... 39 Data Harmonization in CIS A. Pankin, V. Kuzenny .......................................................................... 63 iRank: Integral Ranking of Geographical Information by Semantic, Geographic, and Topological Matching F. Mata, S. Levachkine ......................................................................... 77 GIS as a Basis for Monitoring System A Multi-scale and Multi-modal Transportation GIS for the City of Guangzhou S. Chen, C. Claramunt, C. Ray, J. Tan ................................................. 95 GIS for Profile-Based Context Formation in Situation Management A. Smirnov, N. Shilov, T. Levashova, A. Kashevnik............................ 113 vii

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Table of Contents

Immunocomputing for Geoinformation Fusion and Forecast A. Tarakanov ...................................................................................... 125 Ontologies and Programming Technologies for GIS and GIS Application A Model-driven Approach for Designing Adaptive Web GIS Interfaces M. Angelaccio, A. D’Ambrogio, A. Krek ............................................ 137 Semantic Similarity Applied to Geomorphometric Analysis of Digital Elevation Models M. Moreno-Ibarra, S. Levachkine, M. Torres, R. Quintero, G. Guzman .......................................................................................... 149 Dynamic Models of Geographic Environment Using Ontological Relations M. Martinez, S. Levachkine ................................................................ 165 Geospatial Information Integration Approach Based on Geographic Context Ontologies M. Torres, R. Quintero, S. Levachkine, M. Moreno, G. Guzman ....... 177 An Approach to Comparing Different Ontologies in the Context of Hydrographical Information L. M. Vilches-Blazquez, J. A. Ramos, F. J. Lopez-Pellicer, O. Corcho, J. Nogueras-Iso................................................................ 193 A Web-Service Approach for Multisensor Data Fusion and Geographic Information Systems M. C. Florea, N. Duclos-Hindie, E. Bosse, P. Valin .......................... 209 Monitoring of Terrorist Threat Based on GIS Theoretical Investigation of Terrorism. Ontology Development P. Galjano, V. Popovich ..................................................................... 227 Terrorists: Statistical Profile V. Osipov, Y. Ivakin ............................................................................ 241

Table of Contents

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Geographical Information System for Analysis of Critical Infrastructures and their Hazards due to Terrorism, Man-Originated Catastrophes and Natural Disasters for the City of Gdansk M. Kulawiak, Z. Lubniewski, K. Bikonis, A. Stepnowski .................... 251 Maritime GIS IGIS Capabilities Application to Controlling Polystatic Detection Systems Ensuring Security of Marine Economic Activity V. Ermolaev, S. Kozlovskiy, A. Makshanov ........................................ 265 Real-time Web-based GIS for Analysis, Visualization, and Integration of Marine Environment Data J. Dabrowski, M. Kulawiak, M. Moszynski, K. Bruniecki, L. Kaminski, A. Chybicki, A. Stepnowski ............................................ 277 On Optimizing Search Efforts (Area Effectively Swept) Allocation in the Course of Search and Rescue Operations A. Makshanov, V. Ermolaev ............................................................... 289 Information Assurance and Protection in GIS Design of Entrusting Protocols for Software Protection V. Desnitsky, I. Kotenko...................................................................... 301 Vector Finite Groups as Primitives for Fast Digital Signature Algorithms N. Moldovyan, A. Moldovyan ............................................................. 317 Multisignature Protocols and Problem of Simultaneous Signing a Package of Contracts P. Moldovyanu, E. Dernova, A. Kostina, N. Moldovyan .................... 331 Integrated Usage of Data Mining Methods for Malware Detection D. Komashinskiy, I. Kotenko .............................................................. 343 Schemes for Data and User Authentication in Distributed GIS S. Shtanko, A. Pankin......................................................................... 359 Author Index ............................................................................... 370

Organization

Workshop Charmen General Chairman

Rafael M. Yusupov

St. Petersburg Institute for Informatics and Automation, Russia

Program Committee Chairman

Vasily V. Popovich Manfred Schrenk Christophe Claramunt Kyrill Korolenko

St. Petersburg Institute for Informatics and Automation, Russia MULTIMEDIAPLAN.AT, Vienna, Austria Naval Academy Research Institute Lanveoc-Poulmic, Brest Naval, France NAVSEA, Newport, USA

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Organization

Program Committee Andrienko Gennady Axberg Stefan Badard Thierry Bertolotto Michela Billen Roland Breunig Martin Carswell James D. Engelke Dirk Guibert Eric Hovanov Nikolay Huang Bo Jakobson Gabriel Jiang Bin Krek Alenka Kotenko Igor Kokar Mieczyslaw M. Moldovyan Nikolay Prisyagnuk Sergei Peytchev Evtim Rimashevsky Adam Rodionov Anatoly Smirnov Alexander Sokolov Boris Tarakanov Alexander Tomko Martin Vasyukov Vladimir Zykov Grigory

(Fraunhofer Institute IAIS, Germany) (Swedish National Defense College, Stockholm, Sweden) (University Laval, Quebec, Canada) (School of Computer Science and Informatics, University College Dublin, Ireland) (University of Liege, Belgium) (Research Centre for Geoinformatics and Remote Sensing, University of Osnabrueck, Germany) (Spatial Scientist Digital Media Centre Dublin Institute of Technology, Ireland) (Spatial Planning, member of pakora.net) (Hong Kong Polytechnic) (St. Petersburg State University, Russia) (Department of Geomatics Engineering University of Calgary, Canada) (altusys, Boston, USA) (Division of Geomatics Department of Technology and Build Environment University of Gävle, Gävle, Sweden) (HafenCity University Hamburg Institute for Urban, Regional and Environmental Planning, Germany) (St. Petersburg Institute for Informatics and Automation, Russia) (Northeastern University, Boston, USA) (SPECTR, St. Petersburg, Russia) (Institute of Telecommunications. St. Petersburg, Russia) (The Nottingham Trent University, UK) (VADM, Russian Navy) (RAS, St. Petersburg, Russia) (St. Petersburg Institute for Informatics and Automation, Russia) (St. Petersburg Institute for Informatics and Automation, Russia) (St. Petersburg Institute for Informatics and Automation, Russia) University of Zurich, Switzerland (RADM, Russian Navy) (Naval Academy, St. Petersburg, Russia)

Organization

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Reviewers Andrienko Gennady Axberg Stefan Badard Thierry Bertolotto Michela Billen Roland Breunig Martin Carswell James D. Guibert Eric Hovanov Nikolay Huang Bo Jakobson Gabriel Jiang Bin Krek Alenka Kotenko Igor Moldovyan Nikolay Prisyagnuk Sergei Peytchev Evtim Smirnov Alexander Sokolov Boris Tarakanov Alexander Tomko Martin

(Fraunhofer Institute IAIS, Germany) (Swedish National Defense College, Stockholm, Sweden) (University Laval, Quebec, Canada) (School of Computer Science and Informatics, University College Dublin, Ireland) (University of Liege, Belgium) (Research Centre for Geoinformatics and Remote Sensing, University of Osnabrueck, Germany) (Spatial Scientist Digital Media Centre Dublin Institute of Technology, Ireland) (Hong Kong Polytechnic) (St. Petersburg State University, Russia) (Department of Geomatics Engineering University of Calgary, Canada) (altusys, Boston, USA) (Division of Geomatics Department of Technology and Build Environment University of Gävle, Gävle, Sweden) (HafenCity University Hamburg Institute for Urban, Regional and Environmental Planning, Germany) (St. Petersburg Institute for Informatics and Automation, Russia) (SPECTR, St. Petersburg, Russia) (Institute of Telecommunications. St. Petersburg, Russia) (The Nottingham Trent University, UK) (St. Petersburg Institute for Informatics and Automation, Russia) (St. Petersburg Institute for Informatics and Automation, Russia) (St. Petersburg Institute for Informatics and Automation, Russia) University of Zurich, Switzerland

Visual Analytics for Geographic Analysis, Exemplified by Different Types of Movement Data

Gennady Andrienko and Natalia Andrienko Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS, Schloss Birlinghoven, 53754 Sankt Augustin, Germany

Abstract. Visual analytics is introduced in the sense of a new research discipline defined as the science of analytical reasoning facilitated by interactive visual interfaces. Visual analytics combines automated analysis techniques with interactive visualizations so as to extend the perceptual and cognitive abilities of humans and enable them to extract useful information and derive knowledge from large and complex data, and to solve complex problems. In particular, data and problems involving geospatial components are inherently complex and therefore call for visual analytics approaches. The problems of analyzing data about movement of various discrete objects in geographical space are discussed in detail. The paper considers three types of movement data: data describing movements of a single entity during a long time period, data about simultaneous movements of multiple unrelated entities, and data about simultaneous movements of multiple related entities. The pertinent analysis tasks significantly differ for these types of data. For each type of data, the visual analytics techniques and tools lately developed by the authors are briefly described.

1 Visual Analytics The concept and research discipline of visual analytics emerged in response to the grand challenge posed by the overwhelming and rapidly V.V. Popovich et al. (eds.), Information Fusion and Geographic Information Systems, Lecture Notes in Geoinformation and Cartography. DOI: 10.1007/978-3-642-00304-2_1, © Springer-Verlag Berlin Heidelberg 2009

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G. Andrienko, N. Andrienko

growing amounts of data and information from numerous sources. This includes such diverse types of data as texts (documents, news, Web pages, email, etc.), databases (corporate, government, scientific, etc.), images and video (satellite and aerial images, security observation, traffic monitoring, etc.), sensor measurements (environment, medicine, manufacturing, etc.), and other. People need to make sense from these oceans of heterogeneous data in order to make right and timely decisions. The information may be disparate, incomplete, inconsistent, or dynamically changing. Among the massive bodies of data, relevant information content may be hidden in a few nuggets. A real challenge is to support the analyst in: – – –

Distilling the relevant nuggets of information from disparate information streams Understanding the connections among relevant information Gaining insight from data.

However, current technologies cannot support the scale and complexity of the growing analytical challenge. On the one hand, purely automatic analysis procedures work only for well-defined problems whereas most of the realworld problems are ill-defined. Such problems can only be solved with the participation of human analysts applying their creative and versatile thinking, imagination, multifaceted knowledge and experience, as well as common sense. On the other hand, while the computer performance grows rapidly, the basic human skills and abilities do not change significantly. There are fundamental limits, which are being asymptotically approached. This means that large-scale problems have to be reduced to a scale that humans can comprehend and act on. Hence, the advances in the computer technology in themselves are insufficient. Moreover, they are doomed to be under-utilized unless principally new solutions are found which fundamentally improve the division of labor between humans and machines so that the computational power could amplify the human perceptual and cognitive capabilities. Finding such new solutions is the task of visual analytics. The term “Visual Analytics” stresses the key role of visual representations as the most effective means to convey information to the human mind and prompt human cognition and reasoning. As stated in [11], “An estimated 50% of the brain’s neurons are associated with vision. Visualization aims to put that neurological machinery to work.” Visual analytics is defined as the science of analytical reasoning facilitated by interactive visual interfaces [13]. Visual analytics combines automated analysis techniques with interactive visualizations so as to extend the perceptual and cognitive abilities of humans and enable them to:

Visual Analytics Geographic Analysis, Exemplified Different Types Movement Data – – – –

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Synthesize information and derive insight from massive, dynamic, ambiguous, and often conflicting data Detect the expected and discover the unexpected Provide timely, defensible, and understandable assessments Communicate assessment effectively for action.

Data and problems involving geographical components are an appropriate target for visual analytics [3].

2 Geographic Analysis Geographic analysis, or spatial analysis, explicitly takes into account the spatial localization of the phenomenon under study, and various spatial relationships between components of the phenomenon and between the phenomenon and its environment. Geospatial data are typically massive and complex, as a consequence of the inherent complexity and heterogeneity of the geographical space [2]. Geospatial data need to be treated in specific ways taking into account the particular features of the geographical space, such as spatial autocorrelation, anisotropy, and scale dependence. As the heterogeneity of the space and the variety of properties and relationships occurring within it cannot be adequately represented in a machine-oriented form for fully automatic processing, the geographic analysis relies heavily upon the human analyst’s sense of the space and place, tacit knowledge of their inherent properties and relationships, and space/place-related experiences. These are incorporated into the analysis through the use of an appropriate human-oriented (i.e., visual) representation of the geographical space that serves as an adequate model of reality. However, the size and complexity of the data and problems require combining visualization with computational analysis methods, database queries, data transformations, and other computer-based operations. The goal is to create visual analytics environments for synergetic work of humans and computers where the computational power amplifies the human abilities and is, in turn, directed by human background knowledge and insights gained.

3 An Application: Analysis of Movement in Geographical Space Thanks to the recent progress in positioning and tracking technologies, the data about various mobile objects or agents are currently collected in

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growing amounts. Analysis of such data can yield valuable knowledge about the behaviors of the moving objects and about the environment in which they move. Traditional approaches to visualization and interactive exploration of movement data, such as animated maps [6] or interactive space–time cubes [9, 10], cannot cope with the large amounts of now available movement data. There is a pressing need in appropriate visual analytics methods for movement data. Development of such methods has been a major topic in our recent research. Several example datasets with real movement data of different types have been used: – – –

Data describing movements of a single entity during a long time period Data about simultaneous movements of multiple unrelated entities Data about simultaneous movements of multiple related entities.

It has been found out that the pertinent analysis tasks significantly differ for these types of data, as shown in Table 1. Table 1. Types of movement data and related analysis tasks Data Movements of a single entity Movements of multiple unrelated entities

Movements of multiple related entities

Analysis tasks Analysis of the entity’s behavior: significant places, times and durations of the visits to different places, typical trips, times and durations of the trips, deviations and their reasons (1) Studies of space use, accessibility, permeability, connectivity, major flows, typical routes between places (2) Studies of emerging patterns of collective movement: concentration/dispersion, convergence/divergence, propagation of movement characteristics, etc. Studies of relative movements (approaching, encountering, following, evading, etc.) and interactions between the entities

Due to the difference of the analysis tasks, each type of data requires its own analytical procedures. However, some analytical techniques may be applicable to more than one type of movement data. Let us consider examples of the three aforementioned types of movement data and the possible analyses performed with the use of visual analytics techniques.

Visual Analytics Geographic Analysis, Exemplified Different Types Movement Data

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3.1 Analysis of a Single Entity Movement One of the example datasets that has been used includes positions of a private car that has been GPS-tracked for almost a year. The data have been voluntarily provided by the car owner. An important task in the analysis of individual movement behavior is an extraction of significant places of the moving agent. In case of data about a person, these are the places like home, work, shops, school(s), and/or kindergarten(s) attended by the person’s child or children, homes of the person’s friends and relatives, etc. Significance of a place is indicated by considerable amounts of time spent there and/or repeated visits to this place. Hence, in order to discover the significant places of some moving agent, one should extract the stops, i.e., the time intervals when the agent did not move, and the corresponding spatial positions. This can be done by means of database queries. Then spatial clustering can be applied to the extracted positions of the stops to find the places of repeated stops. To interpret the places, it is useful to take into account the typical times and durations of the stops occurring in these places. Thus, to discover and interpret the significant places of the car owner, first, the positions of the stops lasting three or more hours have been extracted and the spatial clustering tool, that produced two major clusters, was applied. The distribution of the stop times over the days of a week and the hours of a day has been visualized by means of segmented histograms with the segments corresponding to the clusters (Fig. 1). Figure 1 shows that the stops of cluster 1 (grey) occur on all days of the week and the stops of cluster 2 (black) occur from day 1 to day 5, i.e., from Monday to Friday. Figure 1 shows that the stops of cluster 1 occur mostly in the second half of the day; the maximum occurrences are from 1900 to 2000 hours. The stops of cluster 2 occur mostly in the morning hours. (A) 53

(B) 63 50

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Fig. 1 The temporal histograms show the weekly (A) and daily (B) distributions of the personal car’s stops that lasted three or more hours

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Such a distribution proves that cluster 1 is located near the person’s home and cluster 2 is near the person’s work. In a similar way, the places of shorter visits [4] were extracted, analyzed, and interpreted. In particular, in order to find the places of the person’s shopping, the interactive filtering was applied to consider separately the times of visits on week-days and at weekends. The next group of analysis tasks deals with the trips, described by the sequences of recorded positions between the stops. To discover the typical trips, the spatial clustering tool, using appropriate distance functions measuring the degree of similarity between two position sequences, was applied. The cluster analysis is supported by an interactive visual interface, which allows the analyst to interpret the results of the clustering and to direct the work of the clustering tool. Figures 2–4 demonstrate examples of findings resulting from the trip analysis. Figure 2 presents three alternative routes from work to home that have been discovered by clustering the trips according to the routes’ similarity. The clusters of trips are shown on a map in a summarized form. The three selected clusters are shown in orange, blue, and purple colors. Dark grey indicates common parts of two or more routes. The frequency histogram of the trip durations in Fig. 3 shows that the “orange” route typically takes much less time than the other two, what may mean that the person makes intermediate stops on the “blue” and “purple” routes. In Fig. 4, the graduated circles represent the mean times spent in different places along the routes. The two biggest circles are located in two shopping areas that have been previously detected among the other significant places of the person. More details about the trip analysis can be found in [4].

Fig. 2 Three different routes from work to home


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As can be seen from these examples, aggregation and summarization are used in the analysis of large amounts of data, even when the data describe the movement of just a single entity. The use of aggregation and summarization becomes indispensable when it comes to analyzing the movement of hundreds or thousands of entities. Thus, one of the datasets used in this research contains over two million records collected by GPStracking of 17,241 cars in Milan (Italy) during one week (the data have been kindly provided by the Municipality of Milan to be used within the project GeoPKDD). 42

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Fig. 3 The frequency histogram of the trip durations. The colored segments correspond to the clusters of trips shown in Fig. 2

Fig. 4 The graduated circles show the mean times spent in different places along the three selected routes

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3.2 Analysis of Multiple Unrelated Entities’ Movements To approach the subject in a systematic way, a decision was made to introduce a formal model of collective movement of multiple entities as function μ : E × T → S where E is the set of moving entities, T (time) is the continuous set of time moments and S (space) is the set of all possible positions [5, 7]. As a function of two independent variables, μ can be viewed in two complementary ways: – {μ e : T → S e ∈ E}, where each function μ e : T → S describes the movement of a single entity. We call the function μ e the trajectory of the entity e . The decomposition of μ into a set of μ e may thus be called trajectory-oriented view; – {μt : T → S t ∈ T }, where each function μt : E → S describes the spatial positions (and, possibly, additional attributes) of all entities at a time moment t . We call the function μt the traffic situation on the moment t (the term “traffic” is used in an abstract sense and may be applied to any kind of entities). The decomposition of μ into a set of μt may be called traffic-oriented view. Hence, in the trajectory-oriented view, the movement is seen as a set of trajectories of all entities. In the traffic-oriented view, the movement is seen as a time-ordered sequence of traffic situations. For each of the two views, different methods of aggregation and summarization are appropriate. In the traffic-oriented view, it is necessary to aggregate and summarize traffic situations. These basically consist of points in space and point-related characteristics. Therefore, the aggregation and summarization methods suitable for point data can be applied here. In particular, the points can be aggregated by spatial compartments (e.g., cells of a regular grid), by time intervals, which may be defined according to the linear or cyclical model of time, and by values of movement attributes such as direction and speed. The resulting aggregated data can be visualized by means of animated or static maps with the use of coloring or shading, graduated symbols, diagrams, and non-cartographic displays such as temporal histograms. We particularly suggest two cartographic visualization techniques: mosaic diagrams for the exploration of cyclical patterns in traffic variation (Fig. 5) and directional bar diagrams for the exploration of movements in different directions. These and other methods are described in more detail in [1].


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Fig. 5 A map with mosaic diagrams

In the trajectory-oriented view, it is necessary to aggregate and summarize trajectories, which are much more complex objects than points. One of the possible approaches is to group trajectories according to the positions of their starts and ends using a previously defined partitioning of the space into areas. The aggregation is done by putting together the trajectories with the starts and the ends fitting in the same areas. The aggregates can be visualized by means of an origin–destination matrix and by a map with vectors (directed lines) varying in their widths and/or colors or shades according to the characteristics of the aggregates. Thus, Fig. 6 demonstrates an origin–destination matrix where the sizes of the graduated squares in the cells are proportional to the numbers of moves between the respective districts of the city during a selected time interval. The matrix can also show other aggregate characteristics of the groups of trajectories, such as the mean (median, minimum, and maximum) travel time or speed of the movement.

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Fig. 6 An origin–destination matrix

Another kind of aggregation and summarization is used in combination with clustering of trajectories. The method is based on treating trajectories as sequences of moves between small areas, which are defined automatically using characteristic points of the trajectories, i.e., starts, ends, turns, and stops. The areas are built as circles around clusters of characteristic points from multiple trajectories and around isolated points. The aggregation is done by putting together moves connecting the same areas. To visualize a cluster of trajectories, only the moves from the trajectories of this cluster are aggregated. The aggregated moves are shown on a map by vectors (this aggregation-based visualization method has already been used in Figs. 2 and 4). The visualization can be interactively manipulated. Thus, the user may choose to see only the moves occurring in at least k trajectories, where the parameter k can be dynamically changed (Fig. 7). The visual analytics methods for the analysis of massive data about movements of multiple unrelated entities are described in more detail in [12] (cluster analysis) and [1] (aggregation).


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Fig. 7 Summarized representation of the major clusters of trajectories from the suburbs of Milan towards the center on Wednesday morning. Only the moves occurring in at least ten trajectories are visible as a result of interactive filtering

3.3 Analysis of Multiple Related Entities’ Movements In analyzing movements of related entities, the analyst may be interested in uncovering the interactions between the entities in the process of their movement. Movement data usually consist of time-stamped position records and do not contain any explicit information about interactions; hence, it is only possible to detect indications of possible interactions. An important indication is spatial proximity between two or more objects at some time moment or during a time interval. The notion of spatial proximity depends of a number of factors; some of them are listed in Table 2.

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Table 2. Factors influencing the notion of spatial proximity Factor Type of movement Type of relation in focus (analysis task) Place Time

Factor Walking, cycling, driving, … Possibility to observe, possibility to talk, possibility to touch, … City centre, shopping mall, nature park, highway, … Early morning, rush hours, late evening, night, …

An example dataset requiring the analysis of possible interactions between moving agents was collected by tracking movements of 303 school children while they were playing an outdoor mobile game. According to the rules of the game, the children were supposed to visit various places in a city and answer place-related riddles. The players were organised in competing teams. The goals of the analysis were to find out whether the players cooperated within the teams and whether there were conflicts between members of different teams. Detecting and examining indications of possible interactions between the players may help answer these questions. In the case of a large dataset, possible interactions must be extracted from the data by means of computational techniques. A simple and fast computational method has been developed by the authors for extracting possible interactions from movement data. The user is expected to specify threshold values for the spatial and temporal distances between positions of two objects. The method first searches for pairwise interactions. For each pair of objects, it tries to find respective positions in their trajectories such that the spatial and temporal distances between them are within the given thresholds. For the detection of such positions, the following positions of the trajectories are checked. After extracting the pairwise interactions, the method combines interactions sharing a fragment of a trajectory. Extracted interactions may be visualized on a map and in a space–time cube (Fig. 8) prior to inspection by the analyst. More details about the methods for the extraction, visualization, and interactive examination of possible interactions between moving entities are available in [8]. A major problem encountered in developing methods and tools for the analysis of interactions is the large number of possible interactions that can be extracted from movement data. Thus, hundreds of possible interactions (the exact number depends on the chosen threshold values) can be extracted from the data about the mobile game. This exceeds the capacity of the analyst to inspect and interpret each interaction. Hence, there is a need for automated classification of interactions according to their essential properties. For this purpose, it is necessary to define the essential properties of interactions and the ways of extracting these properties from movement data. This is a topic of further research.


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Fig. 8 Visual representation of possible interactions between moving entities

4 Conclusion The mission of visual analytics is to help people analyze large and complex data by amplifying their perceptual and cognitive capabilities. For this purpose, visual analytics combines automated analysis techniques with interactive visualizations. Spatial analysis is an important application area for visual analytics. In the above research, the visual analytics methods and tools for analysis of different varieties of movement data were developed by the authors. Three different types of movement data, the major analysis tasks related to these data types, and the appropriate methods, which combine visual representations and interaction techniques with database processing, clustering, computational aggregation and summarization, and other computational techniques, have been considered in this paper. Acknowledgements The work has been done partly within the EU-funded research project GeoPKDD – Geographic Privacy-aware Knowledge Discovery and Delivery (IST-6FP-014915; http://www.geopkdd.eu) and partly within the research

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project ViAMoD – Visual Spatiotemporal Pattern Analysis of Movement and Event Data, funded by DFG – Deutsche Forschungsgemeinschaft (German Research Foundation) within the Priority Research Programme “Scalable Visual Analytics” (SPP 1335).

References 1. Andrienko G and Andrienko N (2008) Spatio-temporal aggregation for visual analysis of movements. In: Proceedings of IEEE Symposium on Visual Analytics Science and Technology (VAST 2008), IEEE Computer Society Press, pp 51–58 2. Andrienko G, Andrienko N, Dykes J, Fabrikant S, and Wachowicz M (2008) Geovisualization of dynamics, movement and change: Key issues and developing approaches in visualization research. Inform Visual 7(3/4): 173–180 3. Andrienko G, Andrienko N, Jankowski P, Kraak M-J, Keim D, MacEachren A, and Wrobel S (2007) Geovisual analytics for spatial decision support. Setting the research agenda. Int J Geogr Inform Sci 21(8):839–857 4. Andrienko G, Andrienko N, and Wrobel S (2007) Visual analytics tools for analysis of movement data. ACM SIGKDD Explor 9(2):38–46 5. Andrienko N and Andrienko G (2007) Designing visual analytics methods for massive collections of movement data. J Cartogr 42(2):117–138 6. Andrienko N, Andrienko G, and Gatalsky P (2000) Supporting visual exploration of object movement. In: Gesù VD, Levialdi S, Tarantino L (eds) Proceedings of the Working Conference on Advanced Visual Interfaces AVI 2000, Palermo, Italy, ACM Press, pp 217–220 7. Andrienko N, Andrienko G, Pelekis N, and Spaccapietra S (2008) Basic concepts of movement data. In: Giannotti F, Pedreschi D (eds) Mobility, Data Mining and Privacy – Geographic Knowledge Discovery, Berlin, Springer, pp 15–38 8. Andrienko N, Andrienko G, Wachowicz M, and Orellana D (2008) Uncovering interactions between moving objects. In: Cova TJ, Miller HJ, Beard K, Frank AU, Goodchild MF (eds) GIScience, 5th international conference, Proceedings, pp 16–26 9. Kapler T and Wright W (2005) GeoTime information visualization. J Inform Visual 4(2):136–146 10. Kraak M-J (2003) The space-time cube revisited from a geovisualization perspective. In: Proceedings of the 21st International Cartographic Conference, Durban, South Africa, pp 1988–1995


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11. McCormick B, DeFanti T, and Brown M (1987) Definition of visualization. ACM SIGGRAPH Comput Graph 21(6):3 12. Rinzivillo S, Pedreschi D, Nanni M, Giannotti F, Andrienko N, and Andrienko G (2008) Visually-driven analysis of movement data by progressive clustering. Inform Visual 7(3/4):225–239 13. Thomas JJ and Cook KA (2005) Illuminating the Path. IEEE Computer Society

Application of Self-Organizing Maps to the Maritime Environment

Victor J.A.S. Lobo Portuguese Naval Academy, Alfeite, 2810-001 Almada, Portugal, [email protected]

Abstract. Self-Organizing Maps (SOMs), or Kohonen networks, are widely used neural network architecture. This paper starts with a brief overview of how SOMs can be used in different types of problems. A simple and intuitive explanation of how a SOM is trained is provided, together with a formal explanation of the algorithm, and some of the more important parameters are discussed. Finally, an overview of different applications of SOMs in maritime problems is presented. Keywords: Self-organizing maps; SOM; Kohonen networks

1 Introduction Although the term “Self-Organizing Map” has been used to designate a number of different entities, it generally refers to Kohonen’s Self Organizing Map [1], or SOM for short. These maps are also referred to as “Kohonen Neural Networks” [2], “Topological Neural Networks” [3], “Self-organizing Feature Maps (SOFM),” or “Topology preserving feature maps” [1], or some variant of these names. Professor Kohonen worked on auto-associative memory during the 1970s and early 1980s, and presented his SOM algorithm in 1982 [4]. However, it was not until the publication of the second edition of his book “Self-Organization and Associative Memory” in 1988 [5], and his paper named “The Neural Phonetic Typewriter” on IEEE Computer [5] that his V.V. Popovich et al, (eds.), Information Fusion and Geographic Information Systems, Lecture Notes in Geoinformation and Cartography. DOI: 10.1007/978-3-642-00304-2_2, © Springer-Verlag Berlin Heidelberg 2009

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work became widely known. Since then, there have been many excellent papers and books on SOM, but his 2001 book [1] is generally regarded as the main reference on the subject. This book has had very flattering reviews, presenting a thorough covering of the mathematical background for SOM, its physiological interpretation, the basic SOM, developments, and applications. Although Professor Kohonen has retired, his research group maintains a very good web-site at Helsinki’s Technical University at “http:// www.cis.hut.fi/research.” That site contains public domain software, various manuals, papers, technical reports, and a very thorough and searchable list of papers dealing with SOM (available at “http://www.cis.hut.fi/ research/som-bibl” and containing a total of 7,718 references in December 2008). The som_pak programs, that are available with source code, and the Somtoolbox for Matlab, are of particular interest to anyone wanting to experiment with SOM. We strongly recommend a visit to these sites. Kohonen himself describes SOM as a “visualization and analysis tool for high dimensional data.” These are indeed the two most attractive characteristics of SOM, but, as we shall see, it can be used for many other applications. 1.1 What Can a SOM Do? Despite the simplicity of the SOM algorithm, it can and has been used to perform many different tasks, the most common of which are: 1. Clustering (k-means type clustering): This is probably the most common application of SOM, albeit probably not the best. In this context, the SOM is used as an alternative to k-means clustering [6–8], i.e., given a fixed number k of clusters, the SOM will partition the available data into k different groups. As an example, we may want to divide customers into four different groups according to their characteristics, for marketing purposes. The main advantage of SOM in this case is that it is less prone to local minima than the traditional k-means clustering algorithm, and thus can act as a good initialization algorithm for that method. In fact, it can substitute k-means altogether, for as noted in [9], the final stages of the SOM training algorithm are exactly the same as the k-means algorithm. An extra bonus of the SOM algorithm is that the clusters obtained are topologically ordered, i.e., similar clusters are (usually) grouped together.


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2. Exploratory data analysis and visualization: This is, arguably, the most important application of SOM. In this case, the SOM is used as a nonlinear projection algorithm, mapping n-dimensional data onto a one or two dimensional grid. The SOM can thus be an alternative to PCA projections, principal curves, or multidimensional scaling (MDS) algorithms such as Sammon mappings [10]. Different projection algorithms perform different trade-offs when mapping from high to low dimensions, since in all but the most trivial cases some information will be lost. The main advantage of projecting multidimensional data onto one or two dimensions is that we can easily visualize the data in these dimensions. From this visualization, we can identify outliers (data points that are far from other data), identify data that are similar to a given reference, or generally compare different data. If we project data onto one dimension, we may then plot histograms, and thus identify “natural” clusters of data. A similar result may be obtained with a technique closely related to SOM called U-Matrix [11] that can be extended to visualize what can loosely be interpreted as a twodimensional histogram. 3. Ordering of multidimensional data: This type of application makes use of the topological ordering of the SOM to organize a given set of data vectors according to some criteria. As an example, a 1-dimensional SOM can be used to solve the well-known traveling salesman or related problems [12]. Another interesting use of this ordering capacity of a SOM is to create color palettes from pictures. 4. Supervised data classification: The SOM is not meant to be a classifier, and a related technique called linear vector quantization (LVQ) [1] is best suited for this task. However, just like the centroids obtained by a k-means algorithm, a SOM may also be used to supervise classification by labeling the neurons (or units) with the classes of the data that are mapped to it. 5. Sampling: The units of a SOM have a probability distribution that is a function of the probability distribution of the data used for training. Generally, the SOM will over-represent regions of the input space that have a low density, but that is frequently an advantage since it helps detect outliers and novel data patterns. 6. Feature extraction: Since the SOM performs a mapping from a high-dimensional space to a low dimensional one, it may be used for feature extraction. In the simple case, the new features are simply the coordinates of the mapped data point. This is one of the few cases where SOMs with a dimension greater than two are easy to use. 7. Control and/or data sensitive processing: A SOM can be used to select, based on available data, the best model, controller, or data

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processor for a given situation. The main idea behind this type of application is that instead of designing a rather complex controller, multiple simple controllers may be used, each one tuned to a particular type of situation. During the training of the SOM the input data are partitioned into various Voronoi regions, and each of these is used to train or define the parameters of a different controller. 8. Data interpolation: When using the SOM to interpolate data, the output space of the SOM will have the same dimension as the input space, but since the units are ordered on a regular grid, that grid provides a locally linear interpolator for the data. Beyond these more typical applications of SOM, there have been many others, and a complete list is not practical or indeed interesting. An example of an unexpected application is the use of SOM to draw cartograms [13].

2 Basic Principles A SOM is single layer neural network. The name neural network, or more correctly artificial neural network, is due to the historical fact that they were originally inspired by the way biological neurons were believed to work. Although this analogy is, generally speaking, still valid, developments in artificial neural networks and in our knowledge of how biological neurons actually work have led many researchers to refer to the basic computing units of artificial neural networks not as “neurons,” but as “units.” In this paper, to stress the difference between the mathematical model of a biological neuron and our computational units, we will follow the more recent conventions, and refer to them simply as “units.” There are also many terms used to designate the data that are used to train the network, or later to use it. In this paper, we will follow the term most used in the pattern recognition community, which is simply “pattern” or “data pattern.” Different communities will call it “sample,” “instance,” “point,” or “entity.” In a SOM, the units are set along an n-dimensional grid. In most applications, this grid is two-dimensional and rectangular, though many applications use hexagonal grids, and one, three, or more dimensional spaces. In this grid, we can define neighborhoods in what we call the output space, as opposed to the input space of the data patterns. Each unit, being an input layer unit, has as many weights or coefficients as the input patterns, and can thus be regarded as a vector in the same space as the patterns. When we train or use a SOM with a given input pattern, we calculate the distance between that pattern and every unit in the


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Output space Grid of SOM units

xn xn1 xn1 xn3 xn1

Input space Input patterns (n-dimensional) and units

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Output space Grid of SOM units

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Input space Input patterns (n-dimensional), and units

0 -0.5 1.5 1 0.5 0 -0.5 -0.5

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Fig. 1 Basic SOM architecture. On the bottom, the input patterns are shown as a four-dimensional vector (left) or three-dimensional point (right). The units are also points in this input space. On the top, the grid of units is shown (left) together with a U-matrix coloring of a SOM

network. We then select the unit that is closest as the winning unit (or best matching unit – BMU), and say that the pattern is mapped onto that unit. If the SOM has been trained successfully, then patterns that are close in the input space will be mapped to units that are close (or the same) in the output space and, hopefully, vice-versa. Thus, SOM is “topology preserving” in the sense that (as far as possible) neighborhoods are preserved through the mapping process. Generally, no matter how much we train the network, there will always be some difference between any given input pattern and the unit it is mapped to. This is a situation identical to vector quantization, where there is some difference between a pattern and its code-book vector representation. This difference is called quantization error, and is used as a measure of how well map units represent the input patterns. We can look at a SOM as a “rubber surface” that is stretched and bent all over the input space, so as to be close to all the training points in that

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space. In this sense, a SOM is similar to the input layer of a radial basis function neural network (e.g., [14]), a neural gas model [15], or a k-means algorithm. The big difference is that while in these methods there is no notion of “output space” neighborhood (all units are “independent” from each other), in a SOM the units are “tied together” in the output space. It thus imposes an ordering of the units that is not present in the other methods. These ties are equivalent to a strong lateral feedback, common in other competitive learning algorithms. Let us imagine a very simple example, where we have four clusters of three-dimensional training patterns, centered at four of the vertices of the unit cube: (0,0,0), (0,0,1), (1,1,0), and (1,1,1). If we trained a two-dimensional, four node map, we would expect to obtain units centered at those vertices. If we use a larger map, with 16 nodes, for example, we would expect to obtain a map where the units are grouped in clusters of four nodes on each of the vertices (see Fig. 2).

Fig. 2 Left: a 4-unit 2D SOM clustering some vertices of a 3D unit cube. On the far left we can see the units in the input (data) space, and center left in the output (grid) space. Right: a 16-unit SOM clustering the same data

Before training, the units may be initialized randomly. During the first part of training, they are “spread out,” and pulled towards the general area (in the input space) where they will stay. This is usually called the unfolding phase of training. After this phase, the general shape of the network in the input space is defined, and we can then proceed to the fine tuning phase, where we will match the units as far as possible to the input patterns, thus decreasing the quantization error. To visualize the training process, let us follow a two-dimensional to one-dimensional mapping presented in [1]. In this problem, two-dimensional data points are uniformly distributed in a triangle, and a one-dimensional


0

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Fig. 3 2D to 1D mapping by a SOM, from [1]

SOM is trained with these patterns. Figure 4 represents the evolution of the units in the input space. As training proceeds, the line first unfolds (steps 1–100), and then fine-tunes itself to cover the input space.

3 Description of the Training Algorithm 3.1 The Algorithm Let x k (with k = 1 to the number of training patterns N ) be the ndimensional training patterns. Let wij be the unit in position (i, j ) . Let 0 ≤ α ≤ 1 be the learning rate (sometimes referred to as η), and h (wij , wmn , r ) be the neighborhood function (sometimes referred to as Λ or N c ). This neighborhood function has values in [0,1] and is high for units that are close in the output space, and small (or 0) for units far away. It is usual to select a function that is 1 if wij = wmn , monotonically decreases as the distance in the grid between them increases up to a radius r (called neighborhood radius) and is zero from there onwards. Let wbmu be the best matching unit for a given input pattern. The algorithm for training the network is then: For each input pattern xk : 1. Calculate the distances between the pattern x k and all units wij : d ij = x k − wij .

2. Select the nearest unit wij as best matching unit wbmu = wij : d ij = min (d mn ). 3. Update each unit wij according to the rule wij = wij + α h(wbmu , wij , r ) x k − wij .

4. Repeat the process until a certain stopping criterion is met. Usually, the stopping criterion is a fixed number of iterations. To guarantee convergence and stability of the map, the learning rate α and neighborhood radius r are decreased in each iteration, thus converging to zero.

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The distance measure between the vectors is usually the Euclidean distance, but many others can and are used, such as norm-based Minkowski metrics, dot products, director cosines, Tanimoto measures, or Hausdorff distances. 3.2 Neighborhood Functions The neighborhood function provides a bond between a unit and its neighbors, and is responsible for the topological ordering of the map. In fact, without this neighborhood function (or when its radius is zero), the SOM training algorithm is exactly the same as the incremental k-means algorithm [6]. The two most common neighborhood functions are the Gaussian and the square (or bubble) functions:

hg ( wij , wmn , r ) = e

2 2 1 ⎛ (i −n ) +( j −m ) − ⎜ r 2⎜ ⎝

⎞ ⎟ ⎟ ⎠

2

,

⎧⎪ 1 ⇐ (i − n) 2 + ( j − m) 2 ≤ r hs ( wij , wmn , r ) = ⎨ 2 2 ⎪⎩0 ⇐ (i − n) + ( j − m) > r.

In both cases, r decreases to 0 or 1 during training. If r → 0 the final quantization error will be minimized, but the topological ordering may be lost, since the algorithm is performing a k-means clustering. On the other hand, forcing r → 1 will preserve the ordering of the units, but the quantization error will not be minimized. Moreover, in this case, there will be a border effect, by which units close to the border will be dragged to the center, and present higher quantization errors. The algorithm is surprisingly robust to changes in the neighborhood function, and our experience is that it will usually converge to approximately the same final map, whatever our choice, providing the radius and learning rate decrease to 0. The Gaussian neighborhood tends to be more reliable (different initializations tend to converge to the same map), while the bubble neighborhood leads to smaller quantization errors, and is computationally much faster. A theoretic discussion of the effect of neighborhood functions (although only for the one-dimensional case) can be found in [17], and a less rigorous but more general one in [18].


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3.3 Other Parameters and Training Options As mentioned before, training is usually done in two phases: the unfolding phase, and the fine-tuning phase. The algorithm is exactly the same in both cases, but while in the first phase the neighborhood radius and learning rate have rather high values (to allow for a general orientation of the map), in the second phase they will have smaller values, to perform only fine adjustments on the unit’s positions. As a rule of thumb, the initial radius for the first phase should be roughly the length of the smaller side of the map, while for the second it should be the radius of the expected size of clusters in the output space. The size of the map will depend a lot on the particular problem at hand and on the data available. If the SOM is to be used as an alternative to k-means, one unit per desired cluster should be used. For that type of application, a one-dimensional SOM will usually provide the best results [9]. For exploratory data analysis, a larger map should be used. These are sometimes called emergent-SOM or ESOM [19]. Depending on the amount and variability of available data, a rule of thumb could be to use one unit for each 4–20 or more data patterns, but in some cases one might use more units than data patterns (to obtain very clear cut U-Matrices). 3.4 U-Matrices U-Matrices were introduced by Ultsch [11] and are one of the most popular and useful ways of visualizing clusters with a SOM. A U-Matrix is obtained by computing the distance in the input space of units that are neighbors in the output space. If these differences are small, it means that the units are close together, and thus there is a cluster of similar data in that region of the input space. On the other hand, if the distances are large, the units are far apart, and there isn’t much data in that region of the input space. The U-Matrix can thus be seen as a sort of extension of an inverted histogram for multidimensional data projected on a lower dimensional space: low values indicate large concentrations of data and high values indicate sparse regions. U-Matrices are usually presented as colorcoded maps: white regions indicate low values (and thus clusters), while dark regions indicate separations between clusters.

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4 SOM Variants Many different variants of the basic SOM algorithm have been proposed, and a complete review of these is beyond the scope of this paper. Some reviews of these variants have been published [20, 21], and we will overview some of them to show how the basic algorithm can be adapted to different problems. The original SOM algorithm and most of its variants deal with vector data only. Some variants for nonvector data have also been proposed namely the dissimilarity SOM [22], the Kohonen multiple correspondence analysis and the Kohonen algorithm on disjunctive table [23]. For the simpler case of binary valued data, both the original algorithm using 0 and 1 as real numbers and binary variants of SOM produce good results [24, 25]. SOMs have frequently been used to analyze temporal data, such as EEG or Stock Exchange data. In most cases, time can be imbedded into the data vector, and a standard SOM algorithm is used, treating that vector as a simple input pattern. More interesting uses of SOM have been made by changing the learning rule or by changing the topology or structure of the network so as to explicitly take time into consideration. In the former case, the learning rule may, for example, consider only the neighbors of the last BMU as candidates for the next input pattern, or separate the time variable from the rest when computing the similarity. As for changes in topology and structure, some approaches use hierarchical SOMs with different time frames, or include time delay memories in the units. A review of the different ways in which this has been done, together with a proposal for a taxonomy of temporal SOMs, is available in [26]. Geographical information science problems also have a special variable (special location) that should, like time, be treated in a different way. To this end a variant called GeoSOM has been developed [21, 27, 28]. Hierarchical SOMs [29, 30] combine several SOMs to process data at a low level, and then use their outputs as inputs to a high level SOM that fuses the results. In some applications, the notion of output grid is substituted by a more general graph, such as happens in the minimum spanning tree SOM [20], tree-structured SOM [29], or growing cells [31, 32]. The links and concept of output space may even disappear, as happens in the neural gas model [15, 33, 34]. Another important type of variants on the basic SOM algorithm are those that try and overcome the theoretical obstacles raised by the fact that the SOM does not minimize a global energy function. One solution is to


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change the learning rule slightly, as was done in [35]. Another solution is to use a variation of Gaussian mixture models to derive a topologically ordered map, as is done with generative topographic mapping [36]. However, despite the theoretical soundness of these methods, they do not provide significantly better results and are computationally more complex than the original algorithm.

5 Applications in Maritime Environment Given the wide range of capabilities of the SOM there have been many applications of this technique on maritime problems. SOMs have been used quite frequently to cluster and classify satellite images [3, 37–41]. In most cases, the SOM is basically used as a classifier, and each pixel of the satellite image forms a data pattern. When analyzing satellite images, the ground truth (i.e., the real class of a given pixel) is usually established by an expert, and is rather slow, expensive, and prone to errors. Therefore not many classified pixels are available. One advantage of the SOM in this case is that it may be trained with all the data, including nonclassified pixels, and then labeled with only the classified ones. This labeling may then be extended to other units that belong to the same cluster, improving the classification capabilities of the system. Very similar approaches have been made with data that combine satellite images with other data [42], data obtained by radar [43], data obtained by meteorological stations [44], airborne lasers [45], or even data obtained by simulators. The common factor in all these cases is that a two-dimensional map with pixels that are multidimensional vectors is presented to a SOM for clustering and classification. Let us look at one of these in a little more detail, and then overview the problems where these approaches were successfully applied. One application of SOM to satellite images, that concerns reflectance spectra of ocean waters, is presented in [3]. In this case, a 20 × 20 unit probabilistic SOM (or more precisely PSOM) is trained with 43,000 sixdimensional vectors. Each of these corresponds to sampled pixels of a satellite image with five preprocessed frequency bands, and an extra value corresponding to the spatial standard deviation of one of those measurements. A human expert will then label some of the pixels, and these are used to label the SOM units, either directly or indirectly, after these are clustered with a hierarchical clustering algorithm. The authors point out that the method used provides a good overall classification of the data, in part due to the fact that that the probabilistic nature of PSOM allows for a

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confidence level to be assigned to each classification. The PSOM is also considered useful by showing that a lot of resources are dedicated to separating clouds from other pixels, thus leading to the suggestion that the images be preprocessed to remove these clouds. The author’s main interest is in the characterization of Sahara dust, clouds, and other aerosols present over the ocean, and they do not go into great detail on the parameterization of the PSOM. It could be argued that a nonsquare map would lead to a better stabilization of the training process, and that the use of a U-Matrix would help define larger clusters (instead of using hierarchical clustering), but the authors did not follow that path. The SOM has been used in a similar way (i.e., for clustering and classifying data contained in two-dimensional maps or images), in many applications of environmental science, climatology, geology, and oceanography. These include analyzing sea surface temperature [46–49], plankton [50, 51], ocean current patterns [43, 52], estuary and basin dynamics [53], sediment structure [54], atmospheric pressure [55, 56], wind patterns [39], storm systems [41], the El Niño weather conditions [42], clouds [57], ice [53, 58, 59], rainfall [44, 60, 61], oil spills [45], the influence of ocean conditions in droughts [62], and the relationship between sardine abundance and upwelling phenomena [40]. Data concerning fisheries were analyzed in different perspectives using a SOM in [63]. The use of SOM in this case clearly shows the existence of well-defined changes in fisheries over time, and relationships between different species. A more creative use of SOM in shown in [64], where the SOM is used to segment maps of the seafloor obtained with multibeam sonars. The segmented data is then classified with specialized classifiers for each segment. The SOM is thus used to preprocess the data so that multiple simpler or more precise classifiers can be used to obtain the desired results. Although classical harmonical methods can provide good sea level predictions in most cases, those predictions can have rather large errors in basins, estuaries, or regions where weather conditions have a large influence. In those cases, SOMs have been used to predict sea levels with greater accuracy in [65]. Following an approach common in several problems in robotics [66], the SOM has been used to control an underwater autonomous vehicle (AUV) [67–69]. The basic idea in this type of application is that the SOM receives the sensor inputs, and based on that chooses a unit that will provide the guidance for the AUV. The main advantage of the SOM in this case is that each of the units has a quite simple control law (as opposed to a complicated nonlinear controller), and the topological ordering of the SOM makes it relatively robust to noise in the inputs.


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With the increase in maritime traffic, the consequences of accidents, and the availability of vessel traffic systems (VTS), the automatic detection of anomalous behaviors of ships became a pressing problem. This problem was addressed, in [70], where track data (heading, speed, etc.) from navy exercises was used to train a SOM. Clusters were then identified on that SOM, and both suspicious behavior clusters and outliers were flagged as potential threats. The same problem was tackled in a similar way in [71]. In this case, the emphasis in more on visualization of the data, and on estimating the probability of a given situation occurring in the dataset. Also related to ship trajectories, SOMs have been used to plan patrol trajectories of naval vessels in [72]. The approach followed was basically the one used to solve the traveling salesman problem with a SOM (e.g., [12]). In this case, the geographical locations of “incidents” (accidents and illegal fishing) were used as training patterns, and the trajectory obtained tries to maximize the probability of passing in the area where there were “incidents” in the past. In underwater acoustics, SOMs have been used extensively to analyze passive sonar recordings [73–76]. Although ship noise or transient recognition is basically a supervised task, it is very important to detect novelties, and to relate those novelties to known causes. The SOM can provide this by using large maps which will have many unlabeled units. Additionally, it provides an easy to use and understand interface for the operators. Also concerning fluids, although not directly applied to the maritime environment, an interesting use of SOM is given in [77, 78] for analyzing movement in fluids by tracking particles in suspension. The idea is to use successive images of the fluid for training a map, and then infer the movement by observing how the units change from one step to the next.

6 Conclusions An introduction to how a SOM works and how it can be used has been presented. Despite its simplicity, the SOM can be used for a wide variety of applications. Some of its shortcomings were also pointed out, as well as the main issues that must be taken into consideration when using them. An overview of applications in the marine environment has been given, showing that it has successfully been used in many real maritime problems. I believe that its use in this field is still at a preliminary stage, and more and more powerful uses will be given to SOM. It is frequently used simply for k-means type clustering and supervised classification. While those types of applications are useful, I think that the greatest potential of

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SOM is its ability to project and visualize multidimensional data. Many authors have criticized clustering through visualization as too subjective for engineering purposes. I would argue that clustering is intrinsically a subjective problem, and that the human eye and judgment are the best tools available for that task. The computer algorithms should only present the data in a suitable way, which is exactly what a SOM does. I also believe that there is still a lot of potential for using SOM in nonlinear control and routing or piping problems aboard ships. As SOMs become more mainstream, and software for their use becomes more widespread, they will probably be used in creative ways in even more problems.

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Concept for Corporate Information Systems Development Based on GIS Technologies

Vasily Popovich St. Petersburg Institute for Informatics and Automation of the RAS 39, 14 Liniya, St. Petersburg, 199178, Russia, [email protected]

Abstract. This paper covers up-to-date paradigms for the construction of complex information systems – corporate information systems. The concept of the corporate information system used here is understood as any information system realizing the hierarchic system for decision-making support. The problem of corporate information systems is considered under three angles: theoretical basics, basic technologies, and general architecture. At that, the geoinformation systems technology is selected as the backbone basis. The material is based on long-term research carried out in SPIIRAS, in terms of both theory and technology. Many ideas formulated in the paper have already been subjected to practical validation and implementation through international research projects and R&D work for various clients. Keywords: Corporate information system; Intelligent GIS; Data harmonization; Integration and fusion

1 Introduction Many information technologies (IT) professionals remember that quite recently a dominating idea was that introducing the modern computer hard- and software into practice would automatically solve any automation related problem. As noted in [5] and many other up-to-date publications, the time of “naïve IT-romanticism” is over. The efficiency of IT application V.V. Popovich et al, (eds.), Information Fusion and Geographic Information Systems, Lecture Notes in Geoinformation and Cartography. DOI: 10.1007/978-3-642-00304-2_3, © Springer-Verlag Berlin Heidelberg 2009

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is defined by IT specific influence upon the core activities or businesses either of the advanced companies or of the managerial and technical systems rather than by the existence of advanced techniques and standard software. While not going into historical details of IT development for businesses let us note that an aggregate of research and development offers in the given area can be unified into one concept: corporate information systems (CIS). At present this term is not yet stable, and covers a whole range of issues related to automation of various kinds and scales. In most cases it is understood as the development of ERP class systems, e.g., the R/3 system developed by SAP. This paper proposes the concept based on theoretical and technological developments of SPIIRAS carried out during the last decade and aimed at forming a CIS. CIS is understood as the information support system in corporate management. The CIS allows the control of a certain enterprise or a complex managerial and technical system (hereinafter – Enterprise), covering such steps as concept, decision-making, planning, implementation, control, and analysis of the entire Enterprise activity. The purpose of CIS concept development was to work out the system of views, approaches, and technical solutions incorporating a package of measures that regard the development of information support systems for corporate management and assures a maximal realization of the Enterprise potentials at meeting the Enterprise main task or mission. In the light of the above mission, CIS plays the role of a subsystem supporting an operative and correct decision-making at all management levels. It is well known that the Enterprise management system is specified by a great number of complex heterogeneous components, whose target functioning requires realizing complicated horizontal and vertical relationships. The existing methodologies of the information systems creation do not allow any common project to encompass all the aspects of such a system, and the models being built on the above methodologies turn out to be cumbersome enough to make their apprehension and realization as the final software components quite difficult. Therefore, with the above approach realization the analysis of existing methodologies and technological solutions aimed at the complex information systems development was performed. Also, the best existing theoretical considerations substantiating the methodologies for developing the class of information systems for a large-scale enterprise, corporation, or region were analyzed. The above analysis enabled the singling out of three main constituents for the concept of CIS development.

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Firstly, the above scope assumes the development of the CIS theoretical basics. A theoretical basis is a necessary condition for making wellgrounded decisions over the CIS complete life cycle. Basic research allows the maximal realization of the given subject domain experts’ intelligence as well as to competently run the CIS being implemented. Secondly, basic solutions for CIS creation are proposed. Basic solutions are the minimal set of technologies providing the processes’ automation at acquiring, processing, and distributing the information, necessary for making well-grounded managerial decisions concerning the situation in the timescale defined by the tasks’ level (strategical, operational, and tactical) over the complete life cycle of a business process or a management object. And, finally, this is the development of general CIS architecture in the interests of advanced business.

2 CIS Concept: Main Constituents 2.1 CIS Theoretical Basics CIS theoretical basics include scientifically well grounded approaches, mathematical and information models, algorithms, and methods aimed at substantiating the solutions proposed for CIS. The need for developing the CIS theoretical basics has arisen because the existing approaches do not allow for integrally solving the CIS Enterprise-establishing tasks. As the research carried out has shown, the main problem that theoretical basics are called on to solve is the task of processing large bodies of heterogeneous information [1]. The analysis performed of a current status of the components of the Enterprise’s level information systems (IS) showed that the methodology put in the basis of such systems building is targeted first of all at the automation of certain business processes. As a rule, Enterprise IS consists of the autonomous projects aimed at automating some business function. Even if a separate project assumes an information interaction with other projects, it could only be within a specified project and not providing for the existence of general principles of their organization. A typical approach to business process automation is as follows: there appears a need for automation that is documented as the system of requirements based upon the list of expected end results for such automation. Suppose the need for a special form document appears, then for the above document the requirement will govern its content, information sources, creation, and handling regulations. The task of such document forming is

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automated and successfully used later on. More and more similar tasks arise in the area of document forming. In due course, more and more such documents appear, thus increasing the demand for automating their exchange and registration. The main requirement for such a system will be a provision of exchange of the said list of documents between users. The realization of this project faces the problem that different documents are formed by different tools. Because of this the necessity arises to create special components supporting the operations with documents of any separate type. Evidently, should all documents be created on similar basic principles it would be much easier to realize such a system. The given example demonstrates the need for the uniform principles and approaches to the Enterprise’s activity automation. To effectively use the available information and to provide for the justified managerial decisions, it is necessary to develop scientifically grounded methodology, enabling information interaction between the system’s heterogeneous components, using general theoretical approaches to information processing. To solve the given problem, it is proposed to use harmonization, integration, and data fusion concept. The realization of this problem assumes the consideration of several tasks: 1. Substantiation and realization of a unified data representation model. The unified data representation model is the model defining properties and status of the system’s components, as well as the interrelations with other components. It is appropriate to realize the unified data representation model by defining basic notions and their relationships (ontology) over relevant subject areas and/or responsibility fields. The unified data representation model will enable: − − − −

Simplifying the mechanism of heterogeneous data processing Increasing the reliability of the used information Minimizing the time of the required data acquisition Managing the enterprise in operative (real time) mode.

2. The development of new systems’ transformation method. The new systems’ transformation method is a harmonization process enabling conversion of new systems’ information models into a unified information conversion process. So, if the need for automating certain business processes has appeared at the Enterprise, the method’s development would enable implementing new subsystems in CIS through the same algorithm without developing special decisions for each separate case. This would also noticeably simplify the introduction of new subsystems in the future.


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3. The development of a method aimed at transforming the existing system’s components. The method transforming the existing components is a harmonization process of the systems components’ information models into the unified information representation model. The method provides for the identification (set up a correspondence) of the Enterprise’s ontologies. The development of this method will enable the introduction in CIS of the existing Enterprise’s subsystems, the subsystems’ users are accustomed to, thus avoiding extra expense on the personnel training and retraining. The majority of modern management’s problems involve the “information” matter or notion. A vital need for developing the system of views and the system of technologies aimed at solving urgent problems and tasks related to the notion of information has appeared. At the conceptual level and in accordance with advanced tendencies propagated within the information community, it is reasonable to single out three levels or three procedures of information processing (operating) [2]: − − −

Information harmonization Information integration Information fusion.

The history of the information notion goes back to the rise of mankind. The given notion is so versatile and sophisticated that only some theoretical and/or applied aspects could be discussed. Considerable interest in the notions of information and data arose at the appearance of GIS. Perhaps the GIS developers were the first who were faced with the problems of using the heterogeneous and rather bulky information on real or close to real time scales. GIS technologies have appeared quite recently, just several decades ago, and formed a series of urgent and important tasks in data and information processing. Disregarding the fact that a big difference exists between the notions of data and information in this paper, the above distinction will not be underlined because the general mechanisms of their processing are practically identical. The potential mechanisms are reduced to three relatively independent leads: harmonization, integration, and fusion. 2.1.1 Data Harmonization

The given process assumes the definition of basic notions and their relationships (ontology) over respective subject and/or responsibility areas. For instance, division can be performed over the existing areas of knowledge: management, planning, resource distribution, etc. The information harmonization solves the following main tasks:

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Providing access to possibly greater numbers of primary information sources Enabling information transforming into user friendly form (decodings, recognition, translation, …) Providing access to existing information resources.

In a wide sense, the harmonization can be interpreted as data standardization. In the interests of CIS the following examples of information sources could be generally suggested: − − − − − −

Nonformalized information (regular text, bitmapped graphics, photos, etc.) Formalized information (e.g., in XML format) Formalized measurement results (text or digital form) Various formats of databases Cartographical information in specialized formats Media information in various specialized formats.

Graphically, the information harmonization can be illustrated by Fig. 1. Information Source

Information Consumers

Executive Component

User 1 Source 1 X1 h1 := X1

{H} User 2

Source 2 X2

h2 := X2

Source N

User M

Xn hn := Xn

Fig. 1 Data harmonization


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As a rule, an access to each information source is realized by different protocols, methods, and/or mechanisms. The access to Internet/Intranet resources, databases, GPS, GSM-data, archives, analytics, and others can serve as an example. The heart of harmonization is in the realization of understandable data access principles and mechanisms, their unifying, and reducing the types’ number. The World Meteorological Organization (WMO) codes can be given as an example. Currently, the main part of the data is sent out in the form of facsimile telegrams; that greatly hampers the data processing as well as their further use. At present, WMO is converting all data into a unified XML format that considerably simplifies dealing with such data. Orientation of harmonization result to a great number of consumers is a distinctive feature of the information harmonization process. As pointed out in [4], information harmonization is a much more general problem than CIS. For instance, the information about the environment is essentially important in regional, national, European, and global contexts. In the first place it determined by: −

− − −

Global monitoring of the Earth’s surface, natural resources, and other data to be processed and realized in accordance with the Kyoto Protocol Environmental policy in Europe, including environment protection, urban development, protection from natural cataclysms Threat of harmful emissions, geographical hazards and technological risks International cooperation, security policy implemented through developing the maps, and systems for decision-making support.

The development of regional, national, European, or global spatial data infrastructure puts in a claim for information accessibility and exchange. From this follows the demand for standardization and respective technologies’ development. The actual state of affairs brings up the issue of information accessibility for various communities, thus stimulating the efforts in data harmonization through the development of the common data geomodel. Development of the common data geomodel will allow users to access various data sources and use various kinds of software in their own interests. The European geoinformation community has posed a problem of establishing an open body that would coordinate efforts in information harmonization. By the initiative of the British Geological Survey (BGS) and the Geological Survey of Canada (GSC), a meeting took place in Edinburgh in

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November 2003. The representatives of 15 geological agencies from various countries and continents (Europe, America, Asia, Australia) participated in the meeting, and a working group was formed to develop a data model. The above group functions under the auspices of the Commission for the Management and Application of Geoscience Information (CGI); this is a new commission of the International Union of Geological Sciences (IUGS). The working group has established three subgroups: “Conceptual Model/ Interchange,” “Testbed,” and “Classification Requirements.” In 1998 in Germany, a governmental commission, IMAGI (Interministerial Committee for Geo Information), was formed to develop and implement the German National Spatial Database (Geodateninfrastruktur Deuschland: GDI-DE). The main objective of the database being developed is to harmonize and present the necessary geodata by requests via Internet. Today, the following standards can be underlined in the interests of GIS: 1. Open GIS Consortium, GML, ISO standards. 2. ESRI Marine Data Model. 3. OGC Reference Model. 4. Open GIS specifications. Unfortunately, no such common standards exist for CIS, and this is conditioned by the specific nature of each enterprise or enterprises’ system business. Information harmonization supposes the solving the series of tasks, whose totality can be divided into the following groups: 1. Organizational tasks, requiring to define data sources and consumers, data acquisition systems, and user information awareness. 2. Technical tasks. Realizing the protocols and standards by software and technical methods, and data access. 3. Legal issues. Development of license agreements, copyright, data statuses, common information sharing, arranging for security, copying, and intellectual property protection. 4. Economic and social aspects. Providing for various jobs’ funding and assessing the cost of information and services rendered. Definition of information market size and cost, as well as the profit made and its distribution. 2.1.2 Data Integration

Data integration is understood as data fusion (access to information resources) aimed at solving current tasks (modeling) (Fig. 2). Integration inevitably leads to an increase of data bodies. As a rule, it is stipulated by a


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Fig. 2 Data integration

need to operate large data bodies in real time. Integration is performed for the sake of solving a relatively narrow range of tasks. Various formats can serve as the integration examples. For GIS, they are S 57, VPF, and some other specialized formats. By means of these formats, information is represented in a certain form, as structured data arrays. The designation of such data arrays is solving a certain range of tasks. For example, data in the S 57 format are meant for providing the navigation safety in the set region. The SXF data provide for solving the topographic tasks within the Russian Federation. At present, a tendency to develop complex, distributed data arrays based on the XML technology can be observed. OWL (Web Ontology Language) language is the basis of such technology. Access to the data is performed by using different mechanisms and depends on several factors: − −

Required data processing speed (real time or with a certain delay) A need for large data bodies parallel processing and/or visualization.

Depending on the above factors, access is provided directly in the same format the data are stored in. However, an intermediate data transformation is often required. Such need appears, as a rule, in data visualization systems. It is stipulated by the technical constraints of graphic stations and by the performance of the network and/or processors.

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A distinguishing feature of information integration is that the result is aimed at solving a definite class of tasks. 2.1.3 Data Fusion

Gaining qualitatively new information (information body reduction) is the most complicated and poorly studied phase of data transformation and, as a rule, requires an analytical in-depth study of the subject area. In essence, the information fusion is shown in Fig. 3. The information fusion diagram for the monitoring systems, intended for various purposes, is given as an example in Fig. 4. The given figure shows a hierarchic, from bottom to top, change of the information quality. If it is taken into account that monitoring systems are complex and spatially distributed the data fusion

Fig. 3 Data fusion


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Fig. 4 Data fusion in monitoring systems

idea becomes evident. Such a complex system simply cannot function without this mechanism. A distinctive feature of information fusion process is gaining new quality of information and reduction of its body. The levels marked in Fig. 4 [1] are qualitative leaps in information representation. In this case, Hegel’s principle of a transition from quantity to quality is illustrated. However, the nuance here is that no universal mechanisms exist capable of such qualitative transformations. This is rather a whole system of special research, including quite a number of scientific leads. A data fusion system for monitoring systems shown in Fig. 4 can also be applied in the interests of medium and big business. 2.2 Base Technologies for CIS Realization To increase the feasibility of developing various CIS components it is necessary to use software solutions aimed at performing standard tasks appearing in the course of the Enterprise business processes automation, and to develop on their basis applied technical solutions intended for various purposes and scales. SPIIRAS formalizes such solutions as the following basic technologies for CIS developing: − − − − − −

Intelligent geoinformation system (IGIS) System of ontologies Expert system System of documents’ transform System of calculation models Simulation and modeling system.

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The largest part of the tasks facing the society’s different managerial structures is related to spatial data processing. To solve such tasks, the implementation of intelligent geoinformation operating with spatial data is proposed. Intelligent GIS, except for standard functions, possesses the following specific properties: −

− −

−

− −

Support of distributivity and multi-platforming (in particular, on the basis of J2EE technology application), thus providing for the CIS flexibility at using various platforms stipulating software independence of the operating system Multi-level architecture: allows for making complex hybrid information solutions Application of DBMS and other heterogeneous data sources: enables the completeness of the required information at its acquisition from different sources Application of subject area ontologies: enables information structuring to the required level and stores full information avoiding its redundancy Application of expert systems: provides for implementing the intelligent support for making the managerial decisions Application of Web-services and complete support of the SOA concept: realizes the concept of creating “thin” clients.

The system of ontologies is one more basic technology. The ontology is understood as a detailed formalized specification of the structure in a definite subject area. The ontology’s structure is constructed to meet the requirements of the notion’s unity, completeness, and consistency. The ontology is a formal evidence description of notions within the considered subject areas of classes, each class properties describing different properties. The ontology along with a set of classes’ individual instances forms an information basis. Classes are at the centre of ontology. Classes describe notions in the subject area. Properties describe different characteristics of classes and their instances. The system of ontologies is the main information basis of CIS functioning. Use of the ontology provides for a truly unified information space for all CIS constituents. An ontology constituent should provide for realizing the following capabilities: 1. A real world object combines the properties’ multitude: however, each ontology stores a definite set of information about the object, from the point of view of a definite subsystem. Therefore, to obtain the necessary information, it is required to use the multiple inheritance mechanism.


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2. One of the functions supported by CIS is a capability of restoring the system’s state at a certain instant of time. For this purpose, a history of object properties is saved. For efficient realization of status saving and data processing optimization, the partitioning of object constant and varying data is provided for. 3. Different participants of business processes at decision-making have a need for different information about CIS objects. Thus, the participants can be divided into groups based on their significant information. Therefore, the ontology is capable of filtering the information for different user groups. Another basic technology is the expert system (Fig. 5) included in intelligent GIS as a component supporting the decision-making. The expert system, being part of GIS, solves the following tasks: − −

The task, classical for expert systems, to work out recommendations for decision making The less traditional for expert systems task is a modeling process control, including control of object functioning and reaction to events at the upper management level.

Fig. 5 Example of decision-making support process

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The expert decision-making support system enables the representation of all knowledge in the subject area that is necessary for modeling the complex spatial processes. Based upon visual tools providing for a high degree of visualization and control, the expert system is developed as an object-based ontology and rules for the expert systems. This system generates recommendations for decision making, performs an assessment of the decisions made, control of processes and their participants’ actions, and also other types of analysis based on the rules of the expert systems knowledge base. The transform system is intended for the unified representation and information exchange between different CIS subsystems, as well as for an information exchange with external sources. It enables the representation of electronic documents as the aggregate of objects and their links. The documents transform system realizes the following functions: − − − −

Conversion of verbal information into an object one, in particular, from Microsoft Office and Open Office documents Integration of document management tools into a geoinformation system Displaying the objects described in a verbal document on an electronic map Information transfer control.

Each document can be regarded in the document transform system as a separate information flow with its model for data representation. The component’s task is to transform the information contained in the documents into a type of the corresponding ontology. The system for the documents’ transform functionally supplements the existing workflow systems. The existing workflow systems copy the traditional workflow, resulting in duplication, loss of simplicity, and increase in the personnel involved rather than in perfecting the paper workflow. The proposed system of the document content is regarded as an information array with a certain structure. The information is transformed through a respective adapter in the mediator of the ontology system into the information perceived by other system’s components. The workflow system becomes the data source for the system of the subject area ontologies. One more basic technology is the complex system of calculation models (Fig. 6) representing a system of mathematical libraries for a model support of decision making and business processes’ management. The system of calculation models possesses the following properties: − −

To be easily extended for newly appearing task solving To provide a user with the list of realized functions, grouped by subjects and sections, and their mathematical formulae


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Fig. 6 Variant of calculation models’ system realization − −

To perform the selected function To make the system possible to extended by a user or a developer upon the user’s order.

Also, one of the basic technologies is a system for simulation and modeling intended for modeling the complex business processes with an objective of advanced assessment of their efficiency. Systems of scenarios form the basis of this system. Scenarios are based upon specialized ontology, realizing the rules of relations, behavior, and various actions. The system for simulation and modeling provides for the requirements specified by the Enterprise management system: − − − −

Adequacy of simulation and modeling for the required business processes Sensitivity to variables describing factors that essentially affect the business processes Required accuracy and reliability of the modeling results Required operational efficiency for calculations.

The system of simulation and modeling (Fig. 7) is the basis of the Enterprise’s situational center structure and provides modeling of different variants of situation development.

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Fig. 7 Variant for simulation and modeling system

Besides those, other task groups realized by the system are: − −

Tasks related to life cycle substantiation Tasks related to a quantitative substantiation for making managerial decisions about equipment or other assets’ maintenance.

To solve the above tasks, the methods of multi-criteria optimization, simulation, and decision substantiation under uncertainties can be used. If the task initial data are determined and only one criterion for making decisions exists, the method of a single criterion static deterministic task for decision making could be used. In some cases, different types of analytical models can be applied. Thus, using the above basic solutions for automation of the Enterprise’s applied management processes, it can be possible to increase the feasibility of their development as well as of the business efficiency as a whole. 2.3 CIS Architecture CIS architecture represents series of interconnected subsystems and components, whose implementation will allow for the CIS development as a whole system. This architecture was developed with due account for the approaches to systems’ design and development based on the object-based


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approach and service-oriented architectures. The architecture being presented has been evaluated through several research and development projects. Let us briefly review the main components. 2.3.1 Methodological Support

Methodological support is a set of documents describing technology of CIS construction, the user-friendly techniques for selection and use of the proposed technological methods in receiving concrete results. Methodological support assumes a definition of the work goals, its subjects, limits, selection of tools, and methods for its execution, selection of means (resources), and stages of its implementation. The main objective of methodological support is to manage the CIS components development based on reasonable interconnected activity for CIS makers and managers of the Enterprise. Introduction of a correct methodology for project management allows for arriving at a guaranteed positive result, whereby it is required to manage the entire project development process clearly and professionally. Requirements for methodological support. The methodological support should regulate the following aspects: • Regulations describing each business process (Fig. 8) • Description of roles of developers taking part in production and managerial activity of the business processes

Fig. 8 Classification chart of business processes

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• CIS components’ operation instructions that should contain the data about: in what processes, who (what business person) and what uses for the component as well as the detailed manuals. 2.3.2 Mathematical Support

The quantitative support for making a justified decision by the Enterprise’s executives at all management levels is the main function of CIS mathematical support. In addition, mathematical support solves the tasks related to processing and display of incoming and outgoing CIS information. Mathematical support incorporates the list of mathematical models, algorithms, and methods that should be realized in information and analytical, information and calculation, and calculation constituents of special CIS software, as well as the specifications of the above models, algorithms, and methods. 2.3.3 Information Support

Information support (Fig. 9) enables the development of dynamic information models for the Enterprise’s life cycle management systems, containing at each instant of time data corresponding to actual parameters of the object and medium where they function. The dynamic information models of subordinate managed objects should become the components of these models.

Fig. 9 Information support


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System of rules for Dynamic Information Model Keys List Keys +count : int +list : Key +add() : Key +append(in key : Key) +clear() +delete(in key : Key) : int +item(in name : string) : Key +free(in key : Key)

Entities List Entities +count : int +list : Key +add() : Key +append(in entity : Entity) +clear() +delete(in entiny : Entity) : int +item(in id : string) : Entity +free(in entity : Entity)

Union of Entities Union +name : string +id : string +entities : Entities +assign()

1 Single Key Key +datatype : string +id : string +keys : Keys +name : string +value : object +assign(in key : Key)

1

Dynamic Information Model

Single Entity

*

Entity

*

+classtype : string +id : string +keys : Keys +name : string +relations : Relations +assign(in entity : Entity)

Relationship List Relations +count : int +list : Relation +add() : Key +append(in relation : Relation) +clear() +delete(in relation : Relation) : int +role(in relation : Relation) : string 1 Single Relationship Relation +entity : Entity +role : string

Interchange node Union 1 Union 2 Union 3 entity 1 keys relations entity 2 keys relations

<xml>

*

Generalized Data Presentation Model (GDPM) is universal informational and logic metamodelrepresenting to program components structure of essence describing their object field. GDPM sets system of rules for DIM

Generalized Data Interaction Model (GDIM) is a universal informational and logic metamodelrepresenting to program components structure of essence contained in the message/ GDIM provides data interaction between components based on GDPM

Decomposition Structuring Formalization Entities separation

Relations coding

Space building

Common Data Reduction Method (CDRM) to GDPM is a method offering general sequence of activity on data conversion from any component metamodelto GDPM

Fig. 10 Dynamic information model

The dynamic information model (Fig. 10) for each component is based on a certain system of rules representing information objects’ states and their interactions. 2.3.4 Software

A number of requirements for the software have been formed, that it met: Functional requirements 1. To develop a list of services to be rendered by each functional CIS system. 2. To describe the characteristics of the system and its environment. To specify the list of constraints imposed on actions and functions performed by the system, including time constraints, constraints on the system development process, standards. 3. To take into account the parameters typical of the subject area where the system will be used. Nonfunctional requirements 1. Requirements for the software that should describe the software product operational properties, like the system’s performance, required memory capacity, reliability, system’s transferability.

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2. Organizational requirements reflecting the policy and organizational procedures of the Company and software developers (development standards, programming languages, and design methods accompanying documentation). 3. External requirements accounting for the factors that are external to the system being developed and to the process of its development (interaction of the system with other software products, legal requirements). Subject area requirements 1. Requirements for the software that should describe the software product operational properties, the system’s performance, required memory capacity, reliability, system’s transferability. 2. Organizational requirements reflecting the policy and organizational procedures of the Company and software developers (development standards, programming languages, and design methods accompanying documentation). 3. External requirements accounting for the factors that are external to the system being developed and to the process of its development (interaction of the system with other software products, legal requirements). 2.3.5 Engineering Support

Engineering support is understood as an aggregate of all CIS underlying technical tools; a realization variant is given in Fig. 11. Without a detailed analysis of possible technical solutions, consider some of the most important requirements for the engineering support. 1. Requirements for CIS components’ reliability that should include: 1.1. A typical operational model: the quantitative reliability requirements are based on 1.2. Failure criteria in the modes of designated purpose usage, the nofailure operation requirements are based on 1.3. The required time for continuous no-failure operation 1.4. Criteria for the limiting conditions: the requirements for normal operating period are based upon 1.5. Criteria for the CIS components’ protective properties, the safety requirements are based on. 2. Requirements for operation, storage, and maintenance that should include:


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2.1. Requirements for working and limiting operational conditions, determining the range where CIS components keep their parameters within the set standard limits 2.2. Requirements for operational modes 2.3. Requirements for continuous work period 2.4. Requirements for operation in emergency situations 2.5. Requirements for preventing unauthorized use 2.6. Requirements for the tools of operational control system 2.7. Requirements for the maintenance personnel number and professional skills 2.8. Requirements for the operational/maintenance information and reference system.

Fig. 11 A variant of engineering support realization 2.3.6 Security System and Information Protection

Due to the special nature of CIS functioning caused by dealing with classified and commercially confidential information, the security and information protection system is an essential part of the system and should be developed along with the system’s development. The security levels in the system of classified and confidential information should be evaluated in accordance with the respective legal regulations (declared valid at the state and/or branch levels). It is necessary to develop requirements for the CIS components’ information safety including:

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• Revealing the external factors affecting information safety • Forming safety requirements with due account for the currently valid standards. It is necessary to develop the requirements for information safety in CIS including: • Performing a thorough investigation of the Company’s objects • Classification of CIS according to Guiding Document by the State Customs Committee “Automated Systems. Protection against Unauthorised Access to Information. Classification of Automated Systems Information Safety Requirements” • Development of requirements for Companies’ information safety system based on the CIS Classification Statement. 2.3.7 External Relations System

The Enterprise’s corporate information system is not closed, and its functioning assumes an interaction with other information systems. This is why within the design range it is necessary to take into account the requirements for coordination with a number of interacting information systems. 2.3.8 Life Cycle Structure

Life cycle structure (Table 1) is typically practical. In addition, it is of great importance in the process of system development and in achieving mutual understanding between the customer and the developer. Almost the top priority issue in the considered subsystem is arranging for a warranty and post-warranty servicing as well as the operational personnel training at the companies implementing CIS. Table 1. Life cycle stages of CIS components No. 1 2 3 4 5 6

Stage Formation of the assignment Development Realization Operation Support Removal

Description Analysis of demands, selection of a concept, and project solution Design of the system System manufacturing Commissioning and use Provision of the system functioning Use termination, dismounting, system archiving


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3 Conclusion The concept of CIS development proposed in this paper represents the first version for creating the SPIIRAS internal standard for design of complex, heterogeneous, and distributed information systems. The need for such a development was stipulated by the absence of acceptable concepts and paradigms. Today, the proposals on implementing off-the-shelf systems, rather than the concepts of and approaches to their development, can be found. The thesis cannot but admit that cutting-edge technologies start outdoing the progress in basic research, thus being fraught with a risk of picking up the unjustified solutions and eventually resulting in material as well as in entire business losses. SPIIRAS has only started investigations in this field; it is further planned to accomplish a thorough research and to arrive at a deeper theoretic substantiation of the proposed approaches, and at the independent solutions forming, documenting, supporting, and servicing as well as developing a set of practicalities to shape into a set of architectural decisions for various levels and purposes of CIS.

References 1. Llinas J, Bowman C, Rogova G, Steinberg A, Waltz E, and White F (2004) Revisiting the JDL data fusion model II. In: Proceedings of the 7th International Conference Information Fusion 2004 (Fusion 2004), Stockholm, Sweden, pp 1218–1230 2. Popovich V, and Voronin M (2005) Data harmonization, integration and fusion: three sources and three major components of geoinformation technologies. In: Proceedings International Workshop Information Fusion and Geographic Information System, St. Petersburg, Russia, pp 41–47 3. Popovich VV, Potapychev SN, Shaida SS, and Feizov RZ (2007) Mobile information system supporting decision-making at local government level. In: Schrenk M (ed) Proceedings of CORP2007, Vienna, Austria 4. Asch K, Brodaric B, Laxton J, and Robida F (2004) An international initiative for data harmonization in geology. In: 10th EC-GI&GIS Workshop, Warsaw, Poland, p 9 5. Erokhin V (2007) Tools for enterprise architecture management. Open Syst. 3:38–45 6. Valet L, Mauris G, and Bolon P (2000) A statistical overview of recent literature in information fusion. In: Proceedings of the Third International Conference on Information Fusion (Fusion 2000), Paris, France, pp 22–29

Data Harmonization in CIS

Andrey Pankin and Viktor Kuzenny St. Petersburg Institute for Informatics and Automation of the RAS, 39, 14 Liniya, St. Petersburg, 199178, Russia, [email protected]

Abstract. The existing principles of business automation and corporate information systems (CIS) development insufficiently enable the integration of CIS informational subsystems, thus hampering the cooperation of specialists from different departments as well as the acquisition of reliable and complete information. This problem so far lies beyond the range of standard technological solutions. So, to effectively use the available information and to provide for making the justified managerial decisions, it is necessary to develop a scientifically sound methodology that would allow for information interaction between the system’s heterogeneous components using, therefore, general theoretical approaches to information processing. This paper proposes a solution based on the advanced and best elaborated methodology of data harmonization, integration, and fusion. Keywords: Corporative information systems; Data harmonization; Data integration; Data fusion; Business process; Common data representation model (CDRM); Dynamic information model (DIM); Common information interactions model (CIIM); Object-oriented approach; Ontology

1 Introduction Advanced large-scale enterprises and institutions possess a significant amount of heterogeneous information stored in different media or forming a part of their employees’ professional knowledge. A part of this information is stored in different information systems used at the enterprise. At V.V. Popovich et al, (eds.), Information Fusion and Geographic Information Systems, Lecture Notes in Geoinformation and Cartography. DOI: 10.1007/978-3-642-00304-2_4, © Springer-Verlag Berlin Heidelberg 2009

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present these systems are not sufficiently integrated and that is why the information is often duplicated; at the same time such information can be insufficiently complete in any system. Currently, the integration of applications plays a more and more important role in providing for the effective operation of an information environment at the enterprise. The integration speeds up the information processing, decreases the number of errors, and makes information more accessible. Specific integration demanding areas can be identified: − − −

Information exchange between different departments of the enterprise, e.g., office, remote warehouse, store Information exchange between contracting parties, e.g., price lists, sales documents Information exchange between different software products used at the enterprise.

On the one hand a nonavailability of a unified data format prevents interactions between different software products used at the enterprise; on the other hand a nonavailability of the unified subject area description hampers cooperation of the specialists from different departments and contracting parties of the company, as well as the acquisition of reliable and complete information about the business processes’ characteristics. The issue of integration is particularly acute in the case of Corporate information systems (CIS) use at the enterprise as far as the concept of resource management systems implies the existence of a common information space for all the business processes of the enterprise. Consider the solutions available in existing CIS market. SAP R/3 produced by SAP is the most often introduced system, especially in Russia. SAP R/3 includes a set of application modules supporting different business processes of the company and integrated in real time mode. Thus, SAP R/3 advantages incorporate the systems’ modularity, high functional flexibility and availability of integrated manufacturing and project systems; the disadvantages include the complicated documentation, exactly specified requirements for hardware, and high maintenance costs. Consequently, in order to integrate SAP R/3 with other external applications, the expensive services of SAP-consultants or additional training of the programmers would be required. Oracle E-Business Suit is also intended for CIS development and is the main SAP R/3 competitor. A large number of Oracle solutions have been introduced at domestic iron and steel works. Oracle and SAP are the global leaders in a segment of enterprise management systems. Products of both suppliers are referred to as the class of largescale integrated systems and possess broad functionality meeting the


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business demands practically in any industry. However, high costs of licenses, consulting services, and supporting the Oracle- and SAP-based decision-making, especially in the integration with other systems, are quite often the key problems while using these ERP systems. Dynamics NAV developed by Microsoft is also a rather famous representative of CIS. Microsoft Dynamics NAV is an integrated system of ERP intended for different problem solving in financial management, business analysis, industrial management, distribution, clients’ relations and servicing, as well as in e-commerce for small, medium, and large companies. The core advantage of the system is that Microsoft Dynamics NAV is closely integrated with other Microsoft software products and technologies that in turn are the most commonly used office software. The disadvantage of Dynamics NAV is insufficient degree of integration with the systems introduced by other software developers. The common disadvantage of the above considered solutions is that their usage implies a rejection of practices and technologies used by the enterprise before, and this involves significant expenditure along with additional training of the personnel. The analysis of modern CIS has revealed three main problems: 1. Historically, the automation in a majority of existing CIS has been independently performed by different organizational and structural units. In this regard CIS elements allow solving some specific tasks for the enterprise organizational and structural departments: however, most of business processes, as a rule, engage several structural units, thus requiring effective interaction with each other. Moreover, the same information is often duplicated in different CIS subsystems to a certain degree, thus possessing no sufficient completeness in any of the subsystems. 2. During the CIS evolutionary development, the automation was implemented on different technological bases that resulted in complicating the information interaction between different systems and sometimes in their complete impracticability. At present, a large number of different information systems included in CIS often have no compatible formats of the data being processed, and that does not allow for a fully effective management of business processes. 3. Existing principles of the enterprises’ automation and CIS development do not provide for a sufficient integration of the CIS incorporated information subsystems. This is stipulated by a necessity of the resources’ geometric increase of what is needed for the subsystems’ integration. So, the automation tools of the majority of departments are heterogeneous and use different formats for data representation. This leads to a necessity of the resources’ geometric increase of

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what is needed for the subsystems integration at such departments since the departments perform data exchange using “everyone with everyone” principle. This principle is illustrated in Fig. 1a. The number of relational types between functional subsystems in this case can be calculated by the following formula:

N=

n(n − 1 ) , 2

where n is a number of organization departments participating in one business process. The development of a Common Information Model of the integrated CIS will allow the avoiding of this fundamental problem. In this case, for the joint functioning of the organizational departments within one business process such departments should have the same relational type within the common model (Fig. 1b). CIS element 1

CIS element 2

CIS element 3

CIS element 4 CIS system (a)

CIS element 1

CIS element 2

General Information Model

CIS element 3

CIS element 4 CIS system (b)

Fig. 1 Relations between CIS elements at existing and prospective approaches


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Further CIS development is impossible without coping with the above three problems. The fact that their solving is rather a research than a technological problem is of fundamental importance, especially in the light of the absence of standard technological solutions. The use of data harmonization, integration, and fusion methods makes the solving of the above specified problems quite feasible.

2 Theoretical Basics of CIS The theoretical basics of CIS are the scientifically substantiated interrelated models, algorithms, and methods. The main task the theoretical basics are aimed at is the processing of large bodies of heterogeneous information. The need for the CIS theoretical basics appeared because the existing approaches do not allow for solving the problems related to CIS development. The advanced CIS current status analysis showed that the methodology underlying the information systems development is oriented first and foremost to the automation of certain business processes. CIS consists of the autonomous projects designed to automate one or other business function of the enterprise. Even if a separate project assumes the information interaction with other projects, it would be implemented only for a specified project, thus providing for no common principles of their organization. A typical approach to the business processes automation could be as follows. There arises a need for the automation that could be arranged as a system of requirements, based upon the list of the expected final results of such automation. Assume that there arises a need for a certain form document then the need will regulate the document content, information sources for such a document, its forming and work with it. The task of the document development is automated and is further successfully used. More and more similar tasks on document forming emerge. Each such task is successfully used. After a while, when the number of such documents grows, the need arises for automating the documents, exchange and registration. The main requirement for this system will consist of providing the specified documents’ list exchange between the users. This project’s realization faces the problem that different documents are developed by different tools. In this way there emerges a need to develop special components aimed at arranging for work with each separate type of documents. It is quite clear that should all documents be developed using similar basic principles the system would be implemented much easier.

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This example illustrates a necessity for united principles and approaches to automation of CIS operations. To effectively use the available information and provide for making the substantiated managerial decisions it is necessary to use scientifically based methodology that would allow for informational interaction between the system’s heterogeneous components using, therefore, theoretical approaches to information processing. Currently, the methodology of data harmonization, integration, and fusion is the most up-to-date, so its use will be the most efficient in solving the above problems. To realize the methodology of data harmonization, integration, and fusion at the CIS development, it is necessary: 1. To realize the common data representation model (CDRM). GDPM is a model describing properties and states of the system components and also interrelations with other components. CDRM can be realized through definition of basic notions and their interrelations (ontologies) over respective subject areas and/or areas of responsibility. CDRM will allow to: − − − −

Facilitate the mechanism of heterogeneous data processing Increase the reliability of the used information Minimize time of the required data acquisition Manage an enterprise in on-line (real time) mode.

2. To develop a method of new systems transformation. The method of new systems transformation is a process of harmonization allowing for transforming the information models of new systems into DGPM. In as much as a need for certain business processes automation arises at the enterprise, the development of this method will permit the introduction of new subsystems into CIS using the same algorithm and avoiding the development of a new approach to decision making for each separate case. This will also facilitate operations on new systems introduction in the future. 3. To develop a method of the existing system components transformation. The method of the existing components transformation is a process of the system’s components’ information models harmonization to the common DGPM. This method envisages the identification (matching) of the enterprise ontologies. Development of this method will allow for embedding (introducing) the existing customized subsystems into new CIS and avoiding the extra costs for personnel training and retraining.


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3 Unified Information Space as a System of Ontologies Large-scale enterprises and institutions possess significant amount of heterogeneous information being stored on different machine-readable media or being part of the personnel’s professional knowledge. It leads to the following negative consequences: 1. Information duplicating in the different enterprise’s CIS. 2. Nonavailability of the unified data format, complicating the interaction between different informational systems and subsystems of the enterprise during the common task solving. 3. Nonavailability of a unified subject area description, complicating the interaction between different specialists of the enterprise units, and acquisition of reliable and complete information about characteristics of the enterprise business processes. One of the approaches to overcoming these disadvantages consists in the use of the unified information space (UIS) for the whole enterprise or institution. While developing UIS it is required to eliminate information redundancy by applying CDRM to all system components. The authors of this paper suggest using the ontology model as CDRM and realizing UIS as the system of interrelated ontologies. Ontology is understood as a detailed formalized structure specification of a certain subject area. Ontology is based on the following categories: 1. Classes describing the isolated object groups of the subject area with a list of properties characterizing them; 2. Objects are class instances that, as a rule, are matched with real world objects used by the application task; 3. Therefore, both the first and the second can be in certain relationships (understood as a link between objects or classes) and possess properties. Ontologies are characterized by the uniformity, completeness, and consistency of the used notions. The ontology development, in addition to the uniformity of heterogeneous information representation, allows for forming an integral view of the subject area, revealing the missing knowledge components, and increasing the efficiency of its repeated use. Use of the ontologies’ system allows for solving the problem of information redundancy. Construction of the ontologies’ system allows for forming the universal description for the subject area. Ontology by its nature is characterized by orientation towards a definite subject area. At the same time within frameworks of a certain enterprise

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and different business processes it is necessary to solve a large number of heterogeneous tasks of different origin. The result is as follows: 1. Absence of one class forming basis and, consequently, the impossibility of building the unified hierarchical structure. 2. Existence of a great number of objects within UIS frameworks. 3. Large number of different properties of the objects and relationships between them; thus, it is required to consider different sets of properties and relationships for different tasks. 4. Need for an easy UIS modification in order to register different changes to the requirements of business processes implementation. Due to these difficulties it is rather hard to implement and support a single ontology common for all business processes. To resolve this contradiction it is required to develop a system of interrelated ontologies rather than a separate ontology. It should be taken into account that the system of ontologies, being the core of UIS, is intended for providing an access to all system components’ information, so the main requirement to the ontology would be providing for the unified standard of the subject area description that would serve as the basis of CIS development. Therefore, it is necessary to consider peculiarities of the enterprise’s business processes and the possibility of its further restructuring and/or changing the executed functions. The above approach is not unique; the global practice gives many examples of successful introduction and testing of the proposed approach. Examples of the projects based on the system of ontologies include: 1. The CYC (as in en-cyc-lopedia) is a project of Microelectronics and Computer Technology Corporation (MCC), Texas. This project is the basis for systems executing the reasoning process by the ontologies for wide application range depending on the specific subject area. CYC includes large multi-context knowledge bases, effective output mechanisms, set of output means, and a row of special application modules. 2. KACTUS is the European project within ESPIRIT project, targeted at building the methodology of multiple use of knowledge about engineering systems during their life cycle. This methodology is required to use the same knowledge bases for design, evaluation, functioning, support, redesign, and training. KACTUS supports the integrated approach including production and engineering methods, and knowledge engineering methods, based on ontological and computational basis for multiple use of the acquired knowledge concurrently with different applications in technical areas.


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3. Target of TOVE (Toronto Virtual Enterprise) project is to create data model that should: −

− −

Provide for a common terminology in the subject area, whose applications can be used together and understood by each participant of relationships Give an accurate and possibly consistent definition of each term based on the first-order logic Define a semantic task by the axioms’ set that would allow for the automatic answering of the range of questions about the subject area.

TOVE-ontologies represent the integrated model of the subject area, consisting of the following ontologies: operations, states and time, management of resources, products, service, production, price, and quantity. The enterprise reaches its goals through implementing certain business processes. A business process can be defined as a set of logically interdependent actions performed to arrive at a certain result of business activities. The business processes are divided by levels into strategic, operative, and tactical. Each ontology should provide a possibility of its adaption to specific business processes of the enterprise. The following properties should be defined for ontologies of the business processes description: 1. Type of business process (strategic, operative, tactical). 2. Business process identifiers and each identifier determinants. 3. Subjects of the business process management and their identifiers. 4. Data structure for storing the formal descriptions of interrelations between the subjects of the enterprise’s business process management and objects of management and interaction, as well as factors of the above specified interrelations. Therefore, each ontology, except for the business-processes ontology, serves as the basis for respective subsystem development. As a rule, ontologies are described using a certain language. Resource definition framework (RDF) and Web ontology language (OWL) can be distinguished as the languages of ontology description. RDF language has been developed by W3C Consortium (international association for developing the World Wide Web standard). RDF is a convenient tool for data structure formalization. By virtue of its universalism RDF allows for ontology description; however, at the same time it is insufficiently expressive. W3C Consortium has also created OWL ontology language. Ontology in OWL can include the description of classes, their properties and objects.

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OWL has been developed with an objective of information processing, and not only for its presentation in a specified form. At present OWL is completely specified and recommended by W3C to be used as the language for ontology description. When creating the latest developments in the area of search systems, inference systems and formal grammar description were used. On the other hand, since OWL is based on XML, the ontologies implemented using the OWL tools are transferred easily between different types of computers having different operating systems and program languages. Essentially, OWL provides a standard for ontology exchange. Thus, it is preferable to use OWL as the language for ontology description. It is necessary to note that the system of ontologies included in CIS should support the following system capacities: −

−

−

Real world object incorporating a multitude of properties. However, each ontology stores a certain set of information about the object from the point of view of a certain subsystem. Thus, in order to receive information required in progress of a certain business process, it is necessary to use the multiple inheritance mechanism within which framework one object inherits a multitude of properties from different system ontologies. One of the functions supported by CIS is a possibility of the system state recovering at a certain instant of time. This facilitates the recovery of separate components in case of failures and increases the efficiency of the enterprise business processes analysis. For this purpose it is necessary to save a history of the object’s properties’ states. In its turn, to effectively realize the state-saving mechanism and to optimize the information processing, it is necessary to partition the constant and variable information about the object. Evidently, different participants of business processes when decision making need different information about CIS objects. Therefore, time participants can be split into groups based on the information essential for them. Hence, one of CIS functions is the filtration of information for different groups of users.

4 Development of CIS Analysis of subject areas of the enterprise’s activities shows that advanced CIS should have a distributed and mobile structure, and work in a system of real or close to real time. The task of such system development is


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complicated in many respects due to the necessity to store the developed and currently used heterogeneous and uncoordinated decisions regarding the information support. To meet the requirements of the mobility and structure distribution, the information support should allow for constructing the dynamic information model (DIM) of the enterprise management system, that at any instant of time contains data corresponding to the actual parameters of the object and the environment where the system functions. Components of this model should be DIMs of subordinated managed objects. Therefore, time DIM of each component should be arranged as a representation of information objects and their interactions with each other in compliance with a certain system of rules, and the model logic should allow for developing DIM for any system component. Disregarding the specificity of different management system components, each DIM should include CDRM as its basis, and objects of the model should allow for describing all possible aggregates of objects and elements currently used in CIS and to be used in the future system development. Descriptions of classes of information objects along with the list of their specific properties should be formed dynamically in the process of program components functioning without changing the model structure. Since the CIS development envisages the possibility of managerial impacts at any of three levels, the model should meet the demand for all information resources to make substantiated managerial decisions at all management levels (strategic, operative, tactical), at all management stages, and during the entire life cycle of the management object. Based on the above, the following requirements can be set for the common information interactions model (CIIM): 1. Objects of the model should be capable of the realizing of any information interaction between the system components that is necessary for performing the automated business functions. 2. In order to level down the requirements for software and hardware the model should be sufficiently simple, i.e., it should contain a small number of elements. The model should realize a mechanism of data transformation from CDRM into a common data exchange model. The following mechanisms should be used to unite the existing decisions with minimal losses and resources consumption, and to create the system of ontologies providing management support in all business processes at all levels of UIS management and implementation and also to support data harmonization and fusion processes:

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A. Pankin, V. Kuzenny − − − − − −

Universal description of the subject area Multiple inheritance of objects Partitioning of constant and variable data about the object Universal mechanisms of relations Saving the history of the state of the objects’ properties Information filtration for different groups of users.

Universal description of the subject area will permit describtion of the objects in CDRM based on common principles. Universal description of the subject area at information model construction assumes the presence of the objects’ classes system indicating their typical properties and values, a list of possible states defined based on the requirements of the business process and expressed through the values of the object intrinsic properties, and a list of interrelations binding the objects at business function realization. Activities of the enterprise assume a large number of different tasks with participation of the same objects of the information model. It leads to a large number of class-forming bases as in the process of such task solving the objects can be classified by different classification features. Multiple inheritance mechanism provides for the objects description in the information model. Different behavior of the same objects in different tasks leads to another problem: in simultaneous modeling of different business processes the properties of the same object can take different values. The mechanism of partitioning constant and variable data about the object helps to avoid the conflict of the simultaneous change in properties. Universal mechanisms of relationships allows a description of complex relationships between different objects of the information model occurring in the progress of business processes realization. For analysis of the information model dynamics and pattern of its state changes up to individual properties of the objects, and also when needed to recover a state at the given instant of time, it is necessary to use a saving mechanism of state history for object properties. Many users at different levels and assigned different access rights will have access to CIS information resources. To solve their tasks, different groups of users will need different information about the same processes and objects participating in them. Information filtration mechanisms for different groups of users will allow fast access to the required information. Adapted and earlier created systems included in CIS and intended for data exchange should be encapsulated into a respective component realizing CDRM. Transformation of information models of the adapted system into


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CDRM should be implemented by general methods of data transformation into CDRM complying with the following requirements: − − −

The method should offer a common specific sequence of data transformation for all adapted systems included in CIS The method should implement mechanisms proposed by methodology of data harmonization, integration, and fusion The transformation method should be simple enough to level down requirements for software.

Considering the reviewed peculiarities of the subject area and results of analysis of the existing solutions on information bases arrangement, the use of the object-oriented approach seems the most reasonable in CIS development. Object technology expands the conventional method of applications development by new data modeling and programming methods. To reuse codes and to improve the information integrity saving, in the object programming the data and code for their processing are arranged into objects. In using the object-oriented approach in development of information bases, the application programs cycle and function with objects stored in the base that uses the standard object-oriented semantics of the language and operations. Owing to existing standards for the interaction between components, the information resources of the distributed system are combined with each other independent of hardware, software, program languages, operating systems, networks, compilers, and different means of forming queries and reports, and are changed dynamically by object manipulation without loss of the working capacity. However, the concept of object information bases is rather complicated. In addition, at the enterprise there exists a number of decisions based on relational databases. In particular, this will strongly affect the amalgamation of information resources into UIS at operative and tactical levels of management. There appears a need to develop a common methodology on data harmonization, integration, and fusion to include it in a common information space of CIS for the earlier uncovered or outdated information systems. In this case, the combined approach seems reasonable as it will allow for using the advantages of an object-oriented approach, thus not rejecting the standard solutions based on a relational approach. These combined approaches can be based on the following solutions: − −

Object-relational adapters consolidating object-oriented applications and relational information bases Object-relational gates. While applying this method a user interacts with an information base through the language of an object-oriented

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−

information base, and the gate replaces all object-oriented elements of such language by relational components Hybrid object-relational information bases, that can store both conventional table data and objects.

Information support meeting the above listed requirements would allow for describing all business processes within a common information model as well as for storing and timely providing the customer with the information needed for making the substantiated managerial decisions at all management levels (strategic, operative, tactical), as well as at all management stages within the management object life cycle.

5 Conclusion Analysis of advanced information systems of enterprises allows for specifying problems for existing automation principles and CIS development. The main direction for further CIS development should include the process of separate subsystems, including adapted subsystems, integration that is intended for solving complex problems of the enterprise management. The proposed decisions are aimed at reducing the design cost, developing and introducing the separate CIS subsystems, and reducing costs of their modifying to be performed with due account of the changing requirements.

References 1. Pankin AV and Potapychev SN (2003) The object-oriented approach to creation of geoinformational systems. Industry 3(33):108–109 2. Pankin AV and Potapychev SN (2003) Informational system as principal support for decision taking. Innovations 8(65):61–64 3. Pankin AV, Saitov SV, and Ivakin YA (2004) Directions and methods of development of the functional system of the navy electronic workflow. Sea Collect 8:31–33 4. Pankin AV (2004) Integration of the functional system of electronic workflow into geoinformational systems. In: IX St. Petersburg International Conference “Regional Informatics – 2004,” St. Petersburg, Russia 5. Pankin A, Popovich V, Potapichev S and Sorokin R (2005) Intelligent GIS for monitoring systems development. In: CORP2005, Vienna, Austria 6. Pankin A (2005) Integration of heterogeneous information flows circulating in the control loop. In: IF&GIS 2005, St. Petersburg, Russia 7. Pankin A, Popovich V, and Ivakin Y (2006) Data for GIS. In: CORP2006, Vienna, Austria

iRank: Integral Ranking of Geographical Information by Semantic, Geographic, and Topological Matching

Felix Mata and Serguei Levachkine PIIG Lab–Centre for Computing Research, National Polytechnic Institute, Av. Juan de Dios Bátiz s/n, 07738, México, D.F., Mexico, [email protected]

Abstract. Previous geographic information retrieval (GIR) works have used different criteria of a geographical nature to rank the documents retrieved from heterogeneous repositories. The most common approaches consider the characteristics and relationships that describe the geographical objects. However, these criteria process the documents in a separate way (only using their geometric or topologic aspects). In addition, they do not take into account the nature of geographic data (spatial semantics) in the weighting and ranking process which limits the assessment of document relevance. Nevertheless, the ranking can be improved by using approaches integrating the essence and nature of geographical space, i.e., (1) geographical attributes, (2) topological relationships, and (3) spatial semantics that are focused on conceptually describing a geographic object. This paper outlines iRank, a method that integrates these three aspects to rank a document. iRank evaluates documents using three sources of information: GeoOntologies, dictionaries, and topology files. The approach consists of three stages which define the geographical relevance between a query and a document. In the first stage, the relevance is computed by using concepts (GeoOntologies), the second stage uses geographic attributes (dictionaries), and in the last stage, the relevance is processed by considering spatial relationships (vector files). Thus, the major iRank advantage is integral

V.V. Popovich et al, (eds.), Information Fusion and Geographic Information Systems, Lecture Notes in Geoinformation and Cartography. DOI: 10.1007/978-3-642-00304-2_5, © Springer-Verlag Berlin Heidelberg 2009

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F. Mata, S. Levachkine

ranking. The results received by the authors show a better ranking with these criteria than ones that use them separately. Keywords: Integral ranking; Geographic information retrieval; GeoOntology; Spatial semantics; Topological and conceptual matching; Gazetteers

1 Introduction The existing mechanisms for calculating relevance are based on comparing the similarity of a document against a query. For example, the information retrieval (IR) vector model [7] measures the document relevance based on word frequency. Web document relevance is measured using links frequency, while ranking classifies the weighted documents in order of importance. In GIR, the weighting and ranking mechanisms are based on characteristics of geographic objects. Herein, the topology, geographical attributes, and spatial semantics are used because they broadly characterize the geographical nature of an object. However, processing these elements faces two problems: the first one concerns the diversity of information sources (different encoding, formats, and representations) and the second one concerns – spatial semantics (its processing and storage). The first problem can be solved using IR approaches, geographic information systems (GIS), and XML as an interoperability format. The second problem is more complicated because the geographic meaning is expressed in different ways and at different levels of detail. For example, the semantics of relation “near” can be captured based on the user’s perception, and taking references from other objects or using metrics assessing the closeness based on parameters of time, distance, or perspective. Moreover, spatial objects are stored based on one of their features using different sources of information. For example, dictionaries store geographical attributes of an object, while the vector files store spatial relationships of geographical objects (e.g., adjacency), and GeoOntologies (geographical ontologies) store semantic relations according to their geographic hierarchy (continent– country–state) and to possible human perception of its geographic scope (near, next). Thus, the integration of these three aspects allows the assessing of a document according to geographical environment and to its human perception, resulting in an enriched ranking. This is the main motivation of the present work.

iRank: Integral Ranking Geographical Information Semantic, Geographic

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2 Previous Work The problem of calculating the geographic importance of a document has been treated by processing the similarity between two geographical locations, one associated with the query and another with the document [1] and [2]. However, the criteria used have turned out to be insufficient to define the geographical relevance of the document. Basically, this occurs because of the spatial representations heterogeneity and the spatial semantics diversity in interpretations. For example, in [3] the authors used operators with topological and proximity relationships in spatial databases. In [4], Larson proposes a series of geographic operators for queries. A similar approach is adopted in the project SPIRIT [5] having a set of operators for spatial queries using different weighting schemes and determining what method should be applied. For example, the concept “near” is measured using the Euclidean distance, while angular difference is used for “north.” Ontologies have also been applied to measure the relevance of documents of geographic place names, using a query expansion approach [9, 11]. The places conceptualization model (ontology) that measures the similarity between the place name and locations is proposed in [14]. For example, Zaragoza can be referred to Mexico or Spain. Other works combine measures of distances and include semantic relationships [10]. However, weight metrics that are applied separately or in combination with IR classical approaches do not allow to evaluate the document adequately, according to geographic domain. Geographical dictionaries have been used in tasks of recovery and weighting because they contain information that allows the use of spatial queries. For example, in [13], three key components of a dictionary are identified: geographic names, location, and properties. Dictionaries have also been used to disambiguate the queries, transforming place names into geographical coordinates. In [15], the topological distance is processed by relationships of distance and direction to assess the similarity of spatial scenes. On the other hand, in [12], objects are evaluated with respect to their topological similarity. Summing up, the works are not included within the state-of-the-art that integrate GeoOntologies, dictionaries, and vector data as a whole for ranking tasks. This integration, however, would be very useful because these three sources of information are storing objects with different geographical representations, different encoding, and enriched semantics. Then, processing these elements allows for establishing the appropriate criteria for ranking geographic information and strongly motivates the presented research.

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The rest of paper is organized as follows; in Sect. 3, the framework and its modules are described. Section 4 contains the results obtained using the proposed approach. Finally, in Sect. 5, the conclusions and future work are outlined.

3 iRank: Integral Ranking by Semantics, Geographical, and Topological Matching iRank is an integral approach that ranks geographical documents arriving from three information sources: GeoOntologies, topological files, and geographic dictionaries. iRank uses three elements that describe geographical objects, namely, topology, semantics, and geographic properties. iRank consists of three stages, at them the geographical relevance between the geographic query and the document is defined. In the first stage, the relevance is computed by using concepts (GeoOntologies), the second stage uses geographic attributes (dictionaries), and in the last stage, the relevance is processed by considering the spatial relationships (vector data). The goal is to obtain an integral relevance. The following notations to represent a < query > (QG ) , “document” ( DG ), {concept} (CG ) , and [instance] ( I G ) are used. The query format used is a triplet <what, rel, where>, where geographical object corresponds to <what >, while <where> is a geographical reference. < rel> is a spatial relationship between < what > and < where >. For example, for query ; <what > element corresponds to , while < rel> is the spatial relationship < near > , and is <where>. The process begins by comparing a query against a retrieved document. This comparison is performed using concepts representing the geographical objects. For that, GeoOntology is explored in order to find the concept associated with each element of the query and the document. For example, query < Lakes near Guadalajara > and retrieved document “Chapala Lake next Ocotlan” are associated with GeoOntology concepts {Water body}, {City}, and {Municipality}. To establish relevance, these concepts are compared by using confusion metrics [8] evaluating a pair of geographical concepts in GeoOntology. Section 3.1 explains this process in detail. The result is the conceptual relevance of the document to the query. Figure 1 shows modules that compose the framework of iRank.


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Fig. 1 iRank framework

As shown in Fig. 1 when the conceptual relevance is obtained, the next steps are aimed at computing the topological relevance (TopologyRel) and the geographical relevance (GeoRel). These three relevancies are fused to obtain an integral ranking (iRel) of the geographic documents. iRel is calculated by (1): iRel ( QG , DG ) =

ConRel ( Cq , Cd ) + GeoRel ( Gq , Gd ) 3 Topology Rel (Tq ,Td ) + , 3

(1)

where ConRel is the value of conceptual relevance between Cq and Cd. Cq is the concept associated with the query and Cd is the concept associated with the document. For example, “Chapala Lake” vs. “Water body”. GeoRel is the geographical relevance between Gq and Gd. Gq is the concept associated with the query and Gd is a geographic attribute of the ument, for example, “Geographic Area” vs. “Guadalajara.” TopologyRel is the topological relevance. Tq is the concept associated with the query and Td is a tuple of topology file, for example, “Chapala Lake” vs. “Lerma River–basin.” iRel is measured in the range of [0, 1], where unity represents the complete relevance and zero corresponds to the zero relevance. In this way, using the value of integral relevance, the results are weighted to be deployed in a list format in ascending or descending order. The rest of section is organized as follows. In Sect. 3.1 the conceptual ranking, using GeoOntologies

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is explained. Section 3.2 describes the geographic relevance measuring. In Sect. 3.3, the topological relevance is defined. In each section, context vector is used as a mechanism of the rankings integration. Section 4 demonstrates the received results. Finally, in Sect. 5, the conclusions and work proposed for the future are discussed. 3.1 Conceptual Ranking Conceptual ranking is the first stage of iRank. This module measures the document relevance using concepts stored into GeoOntologies, where GeoOntologies are composed of concepts (rivers, lakes), semantic relations (hyperonimy, meronimy), topological relations (connect, adjacency), geographical attributes and instances (an instance is a geographical document). For example, a document about “Lerma River” is an instance of [River] concept. Additionally, with each instance an initial weight (Wi) has been associated. This weight was obtained, using the approach similar to the vector model [7]. In particular, each query was submitted to Google and Yahoo! Answers, and from the obtained results (documents), those whose place name matched up to the label’s name of a concept from GeoOntology have been selected. This process has been made semiautomatical, using a program developed in Ruby language.1 For example, when Google was asked for the majority of results have been related to the “Lerma-Chapala basin.” Then, the documents referred to “Lerma-Chapala basin” were considered most relevant (their initial weight is greater) for the queries that include . Equation (2) shows the calculation of the initial weight. Wi =

Ft Nd

(2)

where Wi is the weight of the concept, Ft is the occurrence frequency of term associated with the concept, within a document and Nd is the number of considered documents. The formula normalizes Wi into interval [0, 1] so that unity represents the maximum value of relevance, while zero is associated with the minimum relevance. Now, the calculation of conceptual relevance will be explained. For that, the following scenario will be considered: a GIS specialist needs to analyze possible flooding in the cities near Chapala Lake. Thus, the geographic data

1

Ruby: Language programming. www.ruby-lang.org/es/.


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are searched for using the following query: QG1 = . Then, the query is processed as follows: (1) Analyze query to identify each element of the triplet. (2) Identify the concepts associated with the document and query. (3) Extract the context for the document and query. (4) Process weights (Wi ) and calculate the conceptual confusion. The first step identifies the elements <what>, , and <where> of the query. The second step uses the algorithm OntoExplore [6] to find in the ontology the concepts matching up to each element of the triplet. For example, for QG1 = OntoExplore finds that is associated with {Urban_Area}, relationship is associated with the concept of {next} and with concept {Lake}. The third step consists of extracting the context of the query and document (their neighbor concepts). In this case, for the retrieved document DG1 = “Ocotlan next Chapala Lake,” “Ocotlan” is associated with the concept {Municipality}, while “Chapala Lake” is linked to concept {Lake}, and the relationship “next” is associated with {near}. Subsequently, context is extracted and stored in a Context Vector (Vc ) . For example, {Water Body} has the following neighbor concepts: {Lake} and {River}, stored into Vc . Figure 2 shows GeoOntology, the query, geographic document, and context vector obtained by OntoExplore.

Fig. 2 Context vector obtained by OntoExplore

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Also, Fig. 2 displays the concepts, their initial relevance (inbox), and their weights. The fourth and final step is aimed at determining the conceptual relevance between document “Ocotlan next Chapala Lake” and query , for that, the following (3) is applied. The value obtained is the conceptual relevance (ConRel). ConRel (ci , c j ) =

Wi1 + Wi 2 , D

(3)

where “ConRel” is the conceptual relevance between ci and cj (e.g., “City” vs. “State”), ci is the concept (from query), and cj is the concept (from document). For example, ci = “City” for “Chapala” and cj = “state” for “Guadalajara.” Wi1 and Wi 2 are the initial weights of the involved concepts. For example, Chapala Lake has an initial weight of 0.78 according to (2). D is the confusion between the concepts ci and cj . For example, if ci = Guadalajara and cj = Chapala, then the node that represents “Chapala” is located, and number of nodes explored to reach the node “Guadalajara” is determined. So, the conceptual relevance (ConRel) between document DG1 = “Ocotlan next Chapala Lake” and query is obtained. The first ranking stage ends here. The next step consists of processing the context vector to weight the documents retrieved from two other information sources. This process is explained in Sects. 3.2 (Geographical Ranking) and 3.3 (Topological Ranking). 3.2 Geographical Ranking Geographical Ranking (GeoRank) is the second stage of iRank. GeoRank measures geographic relevance using geographic properties. Weighting is performed by comparing a query and a document from geographic dictionaries. This process is called geographical relevance (GeoRel). First, ConRel is calculated by processing the context vector from Sect. 3.1 and the geographical properties of the objects included in a query. Then, a two-step process is applied. (1) Form pairs of geographical objects: the first object corresponds to the query and the second one corresponds to the document. (2) Processing weights of geographic objects from Vc .


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To explain this process, the query QG1 = Cities near Chapala Lake and a pair of retrieved documents from dictionaries are considered. The documents are: DG2 = “Urban Area shares Railroad” and DG3 = “Urban Area shares Water Body.” Applying the first step for document DG1, the following pair of objects is formed: “Urban Area” vs. , the relationship pair “share” vs. , and finally “Railroad” vs. . Thus, applying the second step to vs. “City” shows that has a Wi = 0,87 (see Sect. 3.1), while “City” has a Wi = 0.76. An average between these values is calculated, giving the relevance of . Relationship “shares” has Wi = 0.7 with respect to the relationship , while “Water Body” has Wi = 0.67 and has Wi = 0.87. An average between them is calculated, giving the relevance of “Water Body.” The geographical relevance (GeoRel) is obtained by 4. Con Re l (QG , DG ) =

Wc1 + Wc2 + Wc3 , 3

(4)

where GeoRel is the geographical confusion between query QG and the document (DG ) and Wi is the initial weight of each element of the triplet <what, rel, where>. Therefore, applying (4) to DG2 , gives (0.87 + 0.7 + 0)/3 = 0.52, while for DG3 , (0.87 + 0.7 + 0.67)/3 = 0.74 obtained. Figure 3 shows the described process for query QG1 and documents DG2 y DG3 .

Fig. 3 GeoRanking. Documents retrieved from dictionaries, and Context Vector (Vc)

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Figure 3 shows the context vector and its values of relevance as well as the pair of documents retrieved from dictionaries. Then, geographical relevance is calculated by (4); for example, vs. “Water Body,” vs. “railroad,” and vs. “Urban Area.” In this case, r13 and r17 are labels of relationship number of . This is the mechanism of GeoRank. Now, the second stage of ranking ends. The next stage is aimed at ranking the retrieved documents from the last information source (TopologyFiles). This process is explained in Sect. 3.3 (Topological Ranking). 3.3 Topological Ranking The third stage of iRank establishing the topological relevance between the document and the query is named Topological Ranking (TopologyRank). To achieve this, TopologyFiles [8] (a file format that stores topological relationships between two geographic objects) is used. Then, to assess relevance, spatial relationships are classified in three groups according to what is defined in [1] and [16]. The first group deals with spatial relationships of Inclusion (if an object A is in an object B), the second one is Proximity (how close is the object A to the object B), and the third one is Siblings (two concepts are siblings if they have the same father). In the following, the rules for assessing these aspects are defined and then ranked with a value of relevance. 3.3.1 Inclusion

Check if Sd falls within Sq, where Sd is the geographic scope of the document, while Sq is the geographic area of the query. For example, Sq of the query QG1 is “Guadalajara” and Sd of the document DG1 is “Chapala Lake”. Equation (5) is applied to determine that inclusion between “Guadalajara” and “Chapala Lake” is 2/5. ⎧ NumDescendants ( Sd ) + 1 if Sd ⊂ Sq ⎪ Inclusion ( Sq , Sd ) = ⎨ NumDescendants ( Sq ) + 1 ⎪ 0 otherwise. ⎩

(5)

Equation (5) returns values in the interval [0, 1]. The maximum value is when both elements have the same number of descendants (Sd falls within Sq) and the minimum one when Sd has no descendants. NumDescendants (S) + 1 is the number of scopes within S, plus scope itself (that is to say, relations “sub-of-region” in the GeoOntology).


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3.3.2 Siblings

A binary function checks if Sq and Sd are siblings in the GeoOntology, defined by (6). For example, “River” and “Lake” have the same father and therefore are siblings. The maximum value (unity) of the function is when the elements are siblings and the minimum (zero) when they are not siblings. ⎧⎪1, if ∃Sx : parent ( Sq ) = Sx ∧ parent ( Sd ) = Sx , Siblings ( Sq , Sd ) = ⎨ ⎪⎩0, otherwise.

(6)

3.3.3 Proximity

Proximity is the inverse of Euclidean distance between two objects, where the first object belongs to the query, and the second one to the document. It is defined by (7): Proximity ( Sq , Sd ) =

1 , Distance ( Sq , Sd ) 1+ Diagonal ( Sq )

(7)

where Sq is the geographic scope of the geographical reference of the query and Sd is the geographic scope of the object described by the document. For example, query scope of QG1 is Guadalajara city because Chapala Lake lies within Guadalajara city. The allocation of this scope (a numeric value) is obtained semi-automatically and presented in a table, using Java tools and shapefiles in conjunction with the criteria established by a GIS specialist. In addition, the Euclidean distance is normalized by the diagonal of the MBR (minimum bounding rectangle) defined for the geographic area of the query (MBR is a rectangle of minimal size that completely encloses the irregular shape of a region). Now, the explanation proceeds on how to calculate the topologic relevance with the following example which Consider query QG1 = and a pair of retrieved documents, DG4 = “Grijalva River crosses Villahermosa” and DG5 = “Ocotlan next Chapala Lake.” Note, that it is a priori known that the document DG4 is irrelevant and that the document DG5 is relevant to query QG1. Topologic relevance is calculated using the following four steps (1) check if objects belong to the same class; (2) extract the geographic scope of the document and query to assess the proximity, inclusion, and siblings; (3) apply an overlay operation between geographic scopes of the document and the query; and (4) topological

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relevance is calculated by the average of overlay, inclusion, siblings, and proximity. Then, arrive at a match between concepts associated with DG4 and QG1. because both of them are linked with the {City} concept. Figure 4 shows this process.

Fig. 4 Identifying concepts associated with the documents and query

The second step is to extract the geographic scope of the query and documents. For QG1 , the geographic area of is extracted, while for document DG4 the length of Grijalva River is defined. Then, the inclusion, proximity, and siblings for both objects are verified. There is no inclusion (Grijalva River is not within the Chapala Lake) so closeness is zero, siblings function is equal to unity, because River and Lake are water bodies, and Proximity is zero. Then, taking the results of these operations, the relevance value is 1/3. At the third step it is verified whether overlapping between two objects exists. If they do not overlap, the topological relevance is zero. In the case of overlapping, the size of the overlapped geographical area is defined, and this value is considered as its relevance. This operation is displayed in a table where the MBR records of each object are stored. Finally, at the fourth and final step, the results are organized according to the overlapping area in ascending or descending order. The process is the same for the rest of the relationships associated with proximity, according to the involved relationship; previously defined functions are applied to obtain the topological relevance.


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4 Experiments and Results iRank has been tested using documents retrieved by iGIR [6], the system that retrieves documents based on integral matching using three sources of information (the same as in this paper). Nine hundred documents have been used, including 300 of topologyfiles [6], 300 of geographic dictionaries elements, and the elements of GeoOntology. Queries possessing spatial relationships: “near,” “in,” and “within” were considered. The relevance belongs to the interval [0, 1] and five classes for describing the document relevance are established. The first one named “null relevance” for documents with value = 0, the second one is “small relevance” (values from 0.1 to 0.3), the third one range marks “medium relevance” (values from 0.4 to 0.6), and the fourth one is defined as “somewhat relevant” (values from 0.7 to 0.9). Finally, the fifth one is “complete relevance” corresponding to the documents weighted with value of unity. An example of “complete relevance” is shown using query QG1 = with document DG7 = “Guadalajara near Chapala Lake,” because Guadalajara is a city near Chapala Lake. To explain these results, consider Fig. 5 which shows Chapala Lake and municipalities and highways surrounding it.

Fig. 5 Counties and highways surrounding Chapala Lake

Table 1 shows the results obtained by iGIR for query QG1 = “Cities near Chapala Lake” and their ranking values according to GeoRank, TopologyRank, and Concept Rank (iRank). Table 1. Results for the query: Q = {“Cities near Chapala Lake”} G1

Document

GEO RANK

Rank position

TOPOLOGY RANK

Rank position

CONCEPT RANK

Rank position

iRank value

Rank position

Poncitlan Chapala Tizapan C. Régules Tuxcueca Jocotepec V.Carranza Ocotlan Jamay Briseñas

0.964 0.895 0.836 0.796 0.758 0.708 0.698 0.687 0.671 0.652

1 2 3 4 5 6 7 8 9 10

0.891 0.904 0.837 0.810 0.673 0.842 0.679 0.718 0.710 0.639

2 1 4 5 8 3 9 6 7 10

0.42 0.87 0.266 0.256 0.29 0.29 0.27 0.299 0.263 0.29

6 1 2 5 8 4 7 3 10 9

0.758 0.889 0.646 0.620 0.573 0.613 0.549 0.568 0.527 0.548

2 1 3 4 6 5 8 7 10 9

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Table 1 shows rankings corresponding to ten retrieved documents. The last column contains the values generated by iRank. For example, the document “Chapala,” GeoRank places in position 2, while TopologyRank and ConceptRank place it in the first position. The reason for this difference is that GeoRank considers the geographic area of Chapala, located lower than Poncitlan municipality. TopologyRank considers the roads that connect Poncitlan municipality, and Chapala municipality with Chapala Lake. In its turn, ConceptRank considers the name of the municipality that in this case coincides with the name of the lake. By integrating these three criteria, iRank places it in the first position. Another example for discussion is the document of “Jocotepec,” where each of three weighting measures places it in different positions. GeoRank places it in the sixth position because its geographic area is the second largest of ten municipalities. TopologyRank, places it third because it has a road connecting with Chapala Lake. ConceptRank places it in the fourth position according to its semantic relations. By integrating these criteria finally iRank places it in the fifth position.

5 Conclusions and Future Work iRank is a method of integral ranking that weights the retrieved documents obtained from three sources of heterogeneous data: topological files, geographic dictionaries, and conceptualization (the meaning of geographical space for a group of people) of objects contained in the documents and queries. iRank uses confusion metrics by taking advantage of the hierarchical nature of the geographical space, through which one can determine if two objects are similar according to their topology, spatial semantics, and geographic properties. The results show that integrating these aspects can improve the ranking process. However, more experiments, using other topological relationships, for example, those from model of 9 – intersection, would be very useful in future work. The plan is to enrich GeoOntologies with conceptualizations built by GIS communities and Web users. Also, the modules will be designed to process the elements of queries according to other aspects related to the places’ names. Finally, the system’s performance test on larger data collection is needed.


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Acknowledgments The authors of this paper thank the Center for Computing Research (CIC), SIP–IPN, National Polytechnic Institute (IPN), and the Mexican National Council for Science and Technology (CONACYT) for their support.

References 1. Egenhofer MJ, Mark D (2001) Naive geography. In Frank AU, Kuhn W, (eds) Spatial Information Theory: A Theoretical Basis for GIS, vol 988 of Lecture Notes in Computer Science, pp 1–16. Springer, Berlin 2. Jones CB, Alani H, Tudhope D (2001) Geographical information retrieval with ontologies of place. In Proceedings of COSIT-2001, Spatial Information Theory Foundations of Geographic Information Science 3. Nedas K, Egenhofer M (1995) Spatial similarity queries with logical operators. In STD’03 Eighth International Symposium on Spatial and Temporal Databases 4. Larson R (1995) Geographic information retrieval and spatial browsing. Geographic Information Systems and Libraries: Patrons, Maps, and Spatial Information, pp 81–123 5. Vaid S, Jones CB, Joho H, Sanderson M (2005) Spatio-textual indexing for geographical search on the web. In Proceedings of the 9th Int. Symp. on Spatial and Temporal Databases (SSTD), LNCS, vol. 3633, pp 218–235 6. Mata F (2007) Geographic information retrieval by topological, geographical, and conceptual matching, Second International Conference, GeoS 2007, Proceedings. LCNS 4853 Springer 2007, ISBN 978-3-540-76875-3, Mexico City, Mexico 7. Baeza-Yates R, Ribeiro-Neto B (1999) Modern Information Retrieval. ACM Press Series/Addison Wesley, New York 8. Levachkine S, Guzman-Arenas A (2007) Hierarchy as a new data type for qualitative variables: J Expert Systems with Applications 32(3):899–910 9. Jones C, Abdelmoty AI, Fu G (2003) Maintaining ontologies for geographical information retrieval on the web. In Proceedings of on The Move to Meaningful Internet Systems: coopIS, doa, and odbase Ontologies, Databases and Applications of Semantics, odbase’03, LNCS, vol. 2888 10. Clementini E, di Felice P, van Oosterom P (1993) A small set of formal topological relations suitable for end-user interaction. In LNCS 692: Proc. 3rd Int. Symposium on Advances in Spatial Databases, pp 277–295 11. Fu G, Jones CB, Abdelmoty AI (2005) Ontology-based spatial query expansion in information retrieval. In Proceedings of In On the Move to Meaningful Internet Systems 2005: ODBASE 2005, LNCS, vol 3761, pp 1466–1482 12. Belussi A, Catania B, Modesta P (2005)Towards Topological Consistency and Similarity of Multiresolution Geographical Maps GIS’05, Bremen, Germany

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13. Hill L (2000) Core elements of digital gazetteers: Placenames, categories and footprints. Borbinha J, Baker T (eds) Research and Advanced Technology for Digital Libraries, proceedings 14. Jones CB, Harith A, Tudhope D (2001) Geographic information retrieval with ontologies of place. Montello DR (ed) Spatial Information Theory. Foundations of Geographic Information Science. International Conference, COSIT 2001, Springer 15. Burns H, Egenhofer M (1996) Similarity of spatial scenes. In Processing 7th International Symposium on Spatial Data Handling, pp 31–42 16. Andrade L, Silva M (2006) Relevance ranking for geographic IR, Workshop on Geographic Information Retrieval, USA. SIGIR

A Multi-scale and Multi-modal Transportation GIS for the City of Guangzhou

Shaopei Chen, Christophe Claramunt, Cyril Ray, and Jianjun Tan Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, People’s Republic of China, [email protected]

Abstract. The search for better urban living has significantly increased the demand for efficient and sustainable multi-modal transportation systems in large urban areas. This should favor emergence of balanced transportation systems that use each mode for what it does best. However, the development of urban transportation policies partly relies on the availability of appropriate data and then information. The research introduced in this paper proposes a multi-modal and multi-scale data model oriented to the representation of the urban transportation system of the city of Guangzhou in China. The model introduced takes into account different transportation modes and integrates them within a federated data model designed using an object-oriented approach. Such a model allows the development of specialized services designed after a survey and study of users’ and planners’ requirements. The approach is experimented in a district of the city of Guangzhou and validated by a prototype development. This experimental system enables transportation planners and decision-makers to take better decisions effectively, and provides highquality geospatial information-based services to final end-users. Keywords: Transportation GIS; Multi-modal transportation network; Object-oriented data modeling

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1 Introduction Nowadays, the concept of sustainable development becomes a key factor in the planning of modern cities. This trend is closely related to the improvement of the quality of life in a city, including ecological, cultural, political, institutional, social, and economic components without leaving a burden on the future generations [16]. Sustainability influences public policies, and favors the development of better urban environments and improving quality of life. This is crucial as the continuous growth of the world population leads to the emergence of modern megalopoles where urban decision-makers face extremely complex challenges. By 2007, more than 50% of the world’s population lived in urban areas, and most of these dwellers are relying heavily on public transportation modes to meet their mobility needs [18]. Urban transportation is a fundamental means to allow access to jobs, markets, education, health care, and other primary services and leisure; it is a vital asset for the development of modern cities. Urban transportation has been long oriented to individual commuters, as cities were viewed as locations of utmost human interactions with intricate traffic patterns linked to commuting, commercial transactions, and leisure/ cultural activities [17]. Sustainable transportation looks forwards to the efficient transportation of passenger and goods, and to sustainable freight and delivery systems. Transportation planners and decision-makers are increasingly considering multi-modal urban transportation strategies to support sustainable transportation associated with urban development [10]. A multi-modal or inter-modal urban transportation system can be defined as the use of two or more modes involved in the movement of people or goods from origin to destination [5]. It appears that quality of multi-modal urban transportation networks is determined not only by availability of main transportation modes, but also by accessibility to and between these modes and services. Nowadays, the main objective of urban transportation organizations is not only to design, build, manage, and extend transit networks but also to maintain high-quality accessibility to these transportation systems taking into account the value and quality of the services provided to dwellers. This implies reconsidering the methods and approaches that support the development and planning of urban transportation systems. In particular, this brings forward the role of federated information systems, as a resource to provide decision-makers, planners, and end-users with the appropriate information at the right time.

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Nowadays, the evaluation of the efficiency of transportation systems also relies on the availability and close management of performance indicators that should reflect a wide range of social, economic, and environmental objectives [9, 12], among which quality of accessibility to transportation modes, modal interconnections, and diversity of transportation modes are key factors. Quality of public transportation services is influenced by many aspects such as coverage (e.g., portion of households and jobs within 5-min walking distance of a 15-min transit service), service frequency, punctuality, comfort (e.g., portion of trips in which a passenger can sit and portion of transit stops with shelters), affordability (e.g., fares as a portion of minimum wage income), information availability, safety (e.g., injuries per billion passenger-miles), and travel times. Although nonexhaustive, these indicators provide many significant inputs for identifying sustainable transportation development strategies. The need for reliable data and thus information have motivated and favored the application of geographical information systems (GIS) to transportation systems [19]. GIS can be defined as an information system oriented to the integration, modeling, analysis, and visualization of geo-referenced information [1]. GIS-T denotes a specific expression that encompasses all the activities that involve the use of geographic information systems for some aspect of transportation planning and management [4]. Miller and Shaw [14] defined GIS-T as the principles and applications of applying geographic information technologies to transportation problems. GIS-T should help transportation planners and decision-makers to take better decisions, and should provide high-quality spatial information-based services to the end-users. One of the specific peculiarities when designing a GIS-T is that available networks should be represented at different scales and granularities in order to reflect multiple abstraction levels used for either planning or managing tasks, or performing a displacement at the end-user level [13]. Transportation modes are varied as they include street, bus, rail (metro), walking, or cycling service routes and their interconnections. Therefore, a crucial issue when delivering transportation services to endusers is to take into account the possibility of combining these transportation modes. This implies, at the conceptual modeling level, considering semantic and topological connexions between these transportation modes. This represents the static component of a multi-modal and multi-scale GIS-T, to be completed by the dynamic properties of such a system [8]. This implies representing the behavior of discrete mobile objects, e.g., vehicle, people, buses, or metro, within the transportation system, such as a dis placement over a given period of time between an origin area and a destination area [7], and integrating the static and dynamic components of a given network system at different levels of abstractions [6]. At a

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higher level of abstraction, GIS-T models can be combined with origindestination surveys and behavioral frameworks in order to study and understand the transportation patterns and trends that emerge from a given urban system [11]. The objective of the research presented in this paper is to introduce a methodological approach to design and implement a GIS-T applied to the modeling of an urban transportation system. The project is experimented in the context of the urban transportation system of the city of Guangzhou in China. The experiment is realized in close collaboration with the GIS centre of the Guangzhou Institute of Geochemistry, Chinese Academy of Sciences (GIGCAS), and Guangzhou CASample Information Technology Co., Ltd. These two institutions provide information-based data and services for the development and co-management of this project. The research introduces a multi-modal and multi-scale transportation data model, designed at the conceptual and logical levels, and whose objective is to favor a global management and planning approach of the transportation system of the city of Guangzhou. The modeling approach is supported by an objectoriented modeling method. A prototype system is experimented and illustrated. It supports the implementation and delivery of added-value interfaces and services oriented to the promotion of multicriteria selection of transportation modes. The rest of the paper is organised as follows. Section 2 introduces the modeling components of the transportation system while Sect. 3 develops the principles of the multi-modal transportation services developed so far. Finally, Sect. 4 concludes the paper and outlines further work.

2 Towards a Multi-modal Transportation GIS This section introduces the principles retained for the design and development of the multi-modal and multi-scale GIS-T data model. The modeling approach is supported by an extensive study of the transportation patterns and requirements of the city of Guangzhou [3]. This modeling approach is applied to the urban system of the city of Guangzhou using an objectoriented modeling method. The object model is designed by adapting a UML-based case tool initially introduced by Booch et al. [2]. The main transportation modeling components are the bus, metro, and street transit networks. We also consider walking opportunities related to the street network.

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2.1 Bus Transit Network The bus network infrastructure is composed of the bus lines of the city. A bus line spreads on streets with two paths restricted by semantic connectivity, i.e., traffic rules. The main modeling concepts of the bus transit network are as follows (Fig. 1):

Fig. 1 UML-based conceptual view of the bus transit network

• A bus route is a directed path of a bus line. A bus line has two bus routes (i.e., one for each direction). • A bus route is a directed path composed of a sequence of bus route stops and segments. A bus route segment is connected to two bus stops. A bus stop is connected to a side of the street. Bus stops located in a same location of the street whatever the side of the street are commonly aggregated towards the notion of a bus platform.

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2.2 Metro Transit Network The metro transit network of the city of Guangzhou is an underground rapid rail network system made of fixed routes, station halls, platforms, and tunnels. A station hall provides ticket services to passengers. A platform is a boarding site alongside railroad tracks. Tunnels can lead passengers from an underground station to several different locations, i.e., entrances/exits on the ground. Figure 2 illustrates a conceptual view of the spatial entities of the metro transit network using UML-based notations whose main principles are as follows. A metro line is a directed path made of a sequence of metro way stops and metro way segments. A metro way stop is associated with a metro way station. Several metro way stops are aggregated towards the notion of a metro way platform. Last, a metro platform is related to several metro passage ways and entrances. 1

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Fig. 2 Conceptual view of the metro transit network

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2.3 Streets Network The public transit network is represented at different levels of abstraction, and supports different logical views of the transport infrastructure. Streets are represented as logical network objects. The main modeling components of a street network are as follows (Fig. 3): generates from

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• A street is defined by a set of interconnected street segments denoted as “road segment center line” (RSCL) connected to street intersections denoted as “RSCL intersection point” (RSCLIntPnt). Turning directions at street intersections are modeled at the logical level using look-up tables tables that store bidirectional node-link opportunities. • A street segment is divided into several parallel strips by median or other other dividing strips (e.g., barriers). Each parallel strip is represented as a carriageway denoted as a “carriage way center line” (CWCL). Each carriage way has a starting and ending node denoted as “CWCL intersection point” (CWCLIntPnt). • A directional carriage way includes one to several traffic lanes.

2.4 Walking Links Walking links are essential means to support connections between a commuter and a transportation mode, and between different transportation modes. Each public transit route has a sequence of stops for passengers boarding or alighting. Route stops and opportunities are located at the same site, or nearby, thus walking links are essential. For instance, metro and bus routes should be appropriately linked up with walking paths thus defining a notion of service area. Most of the streets of the city of Guangzhou provide sideways for pedestrians. These sideways can be referred to as pedestrian service routes. We introduce the term “walking link” to represent these routes. In order to reduce data redundancy and to avoid an increase of the complexity of the data model, a walking link is modeled at the logical level as a CWCL. These walking links are designated to support bidirectional walking links. Nevertheless, turning restrictions between CWCLs should be different from those between walking links, taking into consideration turning and connection opportunities managed by traffic controls. For example, vehicles may be restricted in making a right turn at a given street intersection, but this control may not be valid for pedestrians. These turning opportunities are represented using look-up tables that stores bidirectional node-link possibilities. Although pedestrians can usually make turns freely at an intersection, some intersections deploy barriers to separate motor vehicle lanes from pedestrian lanes for traffic safety. These pedestrian facilities are usually implemented to provide a passage for pedestrians crossing a street or intersection safely. Such a passage is also modeled by a walking link. Also, metro passageways are designed as walking links, as are shortest walking


links between entrances/exits of a CWCL or a CWCLIntPnt. The modeling concept of a walking link covers a wide range of transportation facilities, including pavements, pedestrian turning opportunities, and passageways towards and inside metro stations, and thus are facilitators for multi-modal transportation.

3 Multi-modal Transportation Services A GIS-T prototype has been developed using ESRI ARCGIS and Map Objects. The conceptual and logical data model has been implemented on top of ESRI Arc SDE, that is, an application server that facilitates storage and management of spatial and nonspatial data. Microsoft SQL Server 2000 has been selected as the Arc SDE-based relational database management system to integrate spatial and nonspatial data. Software and service developments have been performed using ESRI Map Objects and MicrosoftVisual Basic 6.0. Figures that appear from the experiments made in the city of Guangzhou (studies and implementation applied to Tianhe District) clearly show that a combination of several transportation modes dramatically extends the urban coverage of transportation facilities. The multi-modal modeling approach provides a methodological support for the development of multiroute planning. Figure 4 illustrates a typical multi-modal route that involves a bus route, a metro way, and several walking links. Such a multi-modal route involves several constraints that include connection possibilities at the physical level (i.e., possibility of performing a multi-modal connection using walking links and logical connections between L3w Metro station M

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different transportation modalities) and temporal constraints (i.e., derived from public transportation timetables). The evaluation of the “cost” of multi-modal transfers is an important constraint when searching for a relevant route. Transfer costs involve different impedance parameters that should be matched to the logical representation of the multi-modal transportation network. One should also take into account the fact that criteria are spatial (e.g., walking distances between different transportation modes) and time-dependent (e.g., transportation timetables), and that different people may have different expectations. This stresses the fact that in all cases such a system should be considered as a decision-aided system, not a definitive solution provider to a multicriteria decision process. For a given transportation mode, the notion of service area is of crucial importance as it defines the coverage with respect to a population area (Fig. 5, example of a service area defined using a radius of 300 m, and

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Fig. 5 Service area of a metro station


where metro and bus stops are highlighted as possible transfer nodes). This is particularly relevant when the origin and destination of a given route are given as search criteria. The accessibility of public transportation can be evaluated by identifying areas served and not served. The experiments made in the city of Guangzhou clearly show that a combination of several transportation modes dramatically extends the urban coverage of transportation facilities. Figure 6 shows the resulting service coverage areas derived from a combination of metro service and bus service coverage areas. Time is a key measurement and variable for travel planning, and a key variable for measuring transportation service efficiency. Time consumption is usually determined by a function of distance and speed. Although metro timetables are generally respected, buses share roads with other motor vehicles and are affected by traffic conditions and traffic controls, especially in overcrowded urban areas. Therefore, average and expected speeds are given to buses in peak and nonpeak hours; this is at the bus route segment level and fixed according to a study made in the center of Tianhe District, following a study of Wang et al. [20]. Time costs of walking links are given according to an average speed of 5 km per hour in open areas. When searching for a route a commuter can define the maximum walking distance he is ready to perform for a given transfer. The time cost of waiting for a bus is determined by practical experience. As it usually appears that the number of transfers should be limited to a reasonable number in order to not discourage commuters, this value is denoted as the route fare in the city of Guangzhou, i.e., 1, 2, or 3 Yuan.

Metro entrance service coverage

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Fig. 6 Metro station and bus service coverage areas

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As observed in previous studies [15], a given commuter might also prefer to take transportation opportunities with reliable travel times (e.g., metro), rather than potentially faster solutions but dependent on traffic conditions (e.g., bus). Therefore, and to a certain degree, it appears that a multiple coverage of service opportunities is a key factor regarding the quality of transportation services provided to commuters. Figure 7 illustrates the main urban areas covered by multiple examples of multicriteria and multi-modal public transport between an origin and a destination.

Intersection of metro entrances ’and bus stops’service coverage Metro entrance

Bus stop

Fig. 7 Conjunction of metro and bus service coverage areas

Figure 8 illustrates an example of multicriteria and multi-modal public transportation transfer defined between an origin and a destination. The multicriteria route planning (Fig. 8a) includes several criteria such as the possibility of prioritizing a bus route only, a metro route only, and support of transfers or not between transportation modes. The system interface developed for the evaluation of the multi-modal route also gives several options such as the shortest path, shortest walking path, least fare, least time, or least number of stops. This provides a flexible interface where the commuter can define her/his proper constraints. Route proposals are returned using different forms: either using text-based and table instruction forms (Fig. 8b) or using a map presentation that outlines the resulting route (Fig. 8c).


Figure 9 illustrates an example of multi-modal route planning that combines walking paths, bus routes and metro lines also presented to the commuter using map and text-based instructions. This is one of the solutions that present how to get to the destination from the origin as soon as possible, i.e., “least time,” by riding metro, then alighting the metro way and walking to board a bus route, finally walking to the destination. The assignment procedure of bus passenger demand generates demands for additional input information on bus route traffic flows. Forecasted route bus flows should be compared with existing patterns on the transportation network. The GIS-T data model facilitates the analysis of existing bus route flows on each road segment with opposite directions, as individual bus routes are referenced to directed carriage way center lines of road segment. Bus route flows on each directed carriage way indicate the current situation of public transportation services. Regarding a user’s request, a system-generated bus route volume should be compared to bus service conditions, particularly with respect to the spatial distribution of the transportation patterns and their intensity. This information also acts as a background to evaluate travel demands. Moreover, by comparing common suggested routes with existing bus route flows, refinements can be made with

Fig. 8a Multicriteria representation

Fig. 8b Results and text-based instruction

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Legend Origin

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Fig. 10 Bus traffic flows along road segments

respect to route scenarios and suggestions regarding the reengineering of the structure of the current transit network. For instance, Fig. 10 illustrates a heavy concentration of bus routes in a street corridor (i.e., Tianhe Road) in the center of Tianhe District. This indicates that the current bus route volume of Tianhe Road is under heavy pressure, and thus not a perfect candidate solution to be prioritized for multi-modal route planning, and that it also needs to be optimized in future transportation planning studies.

4 Conclusion A crucial issue for sustainable development is still to promote efficient urban transportation systems while reducing their negative impacts. This entails the need for effective transportation policies and services that should also be supported by efficient GIS-T models and services, and evaluated using appropriate methods. The main objective of the research presented in this paper was to develop and present a multi-scale and multi-modal GIS-T applied to the city of Guangzhou. The project developed so far provides several data management and integration facilities such as multi-modal transportation data representation and multi-modal route planning. The approach is supported by an object-oriented spatio-temporal modeling

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approach, based on an integration of several transportation modes, either public or private. The transportation services delivered provide several multi-modal transportation choices that prioritized multicriteria assessment of commuter options. The model has been applied to the city of Guangzhou, and validated by a prototype currently experimented in the transportation units of the city. The implemented approach and system provides several levels of services: a decision-aided system for urban planners and decision-makers, and a flexible interface for multiroute planning at the end-user level. Several avenues of research are still under progress or considered for further work, amongst them integration of realtime transportation data within a multiroute search process, and application of the methodological approach to planning scenarios oriented to the development of the transit network system.

References 1. Aronoff S (1989) Geographic Information Systems: A Management Perspective. Ottawa, Canada, WDL Publications 2. Booch G, Rumbaugh J, and Jacobson I (1999) The Unified Modeling Language Users Guide. Reading, Massachusetts, USA, Addison-Wesley 3. Chen S (2008) Multi-scale and Multi-modal GIS-T data model: A case study of the city of Guangzhou, China. Unpublished Ph.D. Report, Naval Academy Research Institute, Brest, France 4. Curtin K, Noronham V, Goodchild MF, and Grise S (2003). Arc GIS Transportation GIS Model, Redlands, California, USA, ESRI publications 5. Dewitt W and Clinger J (2000) Intermodal Freight Transportation, available at http://www.nationalacademies.org/trb/publications/millennium/00061.pdf 6. Etches A, Claramunt C, Bargiela A, and Kosonen I (1999) An interoperable TGIS model for traffic systems. In: Gittings B (ed), Innovations in GIS 6, Integrating Information Infrastructures with GI Technology, London, Taylor & Francis, pp 217–228 7. Fletcher D (1987) Modelling GIS transportation networks. In: Proceedings of the 25th Annual Meeting of the Urban and Regional Information Systems Association, pp 84–92 8. Goodchild MF (1999) GIS and transportation: status and challenges. Keynote address, International Workshop on GIS-T and ITS, Chinese University of Hong Kong, Hong Kong, China 9. Gudmundsson H (2001) Indicators and Performance Measures for Transportation, Environment and Sustainability in North America. National Environmental Research Institute, Roskilde, Denmark, available at www.dmu.dk/ 1_viden/2_Publikationer/3_arbrapporter/default.asp 10. Krygsman S (2004) Activity and travel choice(s) in multi-modal public transport systems. Unpublished Ph.D. Report, Utrecht University, The Netherlands


11. Lee–Gosselin M and Doherty ST (2005) Integrated Land-Use and Transportation Models, Oxford, UK, Elsevier 12. Litman T (2003) Mobility Management. Sustainable Transport Sourcebook. The Sustainable Urban Transport Project in Asia and GTZ; available at www.vtpi.org/gtz_module.pdf 13. Mac Cormack E and Nyerges T (1997) What transportation modeling needs from a GIS: A conceptual framework. Transport Plan Technol 21:5–23 14. Miller HJ and Shaw SL (2001) Geographic Information Systems for Transportation: Principles and Applications. New York, Oxford University Press 15. Peytchev E and Claramunt C (2001) Experiences in building decision support systems for traffic and transportation GIS. In: Proceedings of the 9th International ACM GIS Conference, Aref WG (ed), ACM Press, Atlanta, pp 154–159 16. Rees WE and Roseland M (1991) Sustainable Communities: Planning for the 21st Century. Plan Canada 17. Rodrigue JP (2006) The Geography of Transport Systems. In: Comtois C and Slack B (eds), New York, Routledge 18. Stella F, Viganò V, and Bogni D (2006) An integrated forecasting and regularization framework for light rail transit systems. Intell Transport Syst 10(2): 59–73 19. Thill JC (2000) Geographic Information Systems in Transportation Research, Oxford, UK, Elsevier 20. Wang B, Li JW, and Deng XD (2006) The new idea of advancing LOS of PT in Urban Central area – A case study of Circle Light Bus in Tianhe Area. Chongqing Jiaotong University 25(4):113–115

GIS for Profile-Based Context Formation in Situation Management

Alexander Smirnov, Nikolay Shilov, Tatiana Levashova, and Alexey Kashevnik St. Petersburg Institute for Informatics and Automation of the RAS, 39, 14 Liniya, St. Petersburg, 199178, Russia, [email protected]

Abstract. Geographical and related information stored in the profiles of coalition operation members is of highest importance. The above information can be processed in order to build actual context for the current situation and create more efficient action plans. The profiles are assumed to contain such information as transportation means available, their current coordinates, availability, etc. Through inquiries of a geographical information system (GIS), the following information can be obtained: available roads (traffic management systems can also provide traffic situation information), current weather conditions (wind, temperature, precipitation), time of sunrise and sunset. Keywords: Geographical information system; Profiles; Context

1 Introduction Critical aspects of situation management incorporate managing and controlling sources of information, processing real-time or near real-time streams of events, representing and integrating low-level events and higher level concepts, multisource information fusion, information representation that maximizes human comprehension, and reasoning on what is happening and what is important [1, 2]. In this paper, the situation management is considered to govern the following type, of operations: medical care, evacuation, fire fighting, and accident investigation. These operations can be implemented either by V.V. Popovich et al, (eds.), Information Fusion and Geographic Information Systems, Lecture Notes in Geoinformation and Cartography. DOI: 10.1007/978-3-642-00304-2_7, © Springer-Verlag Berlin Heidelberg 2009

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public organization or by different private organizations. Units that perform these operations are referred to as operation members. The paper proposes an approach based on technologies like context management, GIS, and profiling aimed at situation management. Two types of contexts are considered: abstract (defining structure of the problem/ situation) and operational (defining parameters). The operational context is based on the abstract context and information obtained from sensors and other sources. In real life situations it is often necessary to take into account a continuously changing traffic situation (e.g., to take into account traffic jams, closed roads, etc.) that makes the problem more complex and requires its solving in real-time. In this regard the operational context is constantly updated to provide up-to-date information for situation management. Operation member profiles contain such information as transportation available, current geographical coordinates, competencies, and operation member preferences. Competencies are described by such information as operation member capabilities, capacities, price-list in case of implementation by private organization, and implementation speed. Preferences determine constraints preferable for operation members. The paper is structured as follows. In the second section a service-oriented approach to context formation is discussed. Then the operation member profile model is presented. The case study is described in the fourth section. The most important results are summarized in the conclusion.

2 A Service-Oriented Approach to Context Formation The approach is built around central heavy application ontology (AO) for the domain of interests. The problems formalized in AO are proposed to be interpreted as a constraint satisfaction problem (CSP). CSPs are applied to modeling and solving a wide range of applications such as planning, scheduling, resource sharing, etc. To ensure compatibility of ontology-based knowledge representation and CSP, AO is specified by the formalism of object-oriented constraint networks (OOCN) [3]. CSP model consists of a set of variables; a set of possible values for each variable (its domain); and a set of constraints restricting the values that the variables can simultaneously take. According to the OOCN-formalism, knowledge is represented by sets of classes, class attributes, attribute domains, and constraints. The AO is made up of two constituents: domain knowledge and task knowledge. The domain knowledge represents conceptual knowledge. The task knowledge formalizes logistics problems. In AO the tasks are

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represented by classes, input, and output arguments of tasks are represented by class attributes. Tasks are configured in accordance with tasksubtask decomposition structure, where tasks and their subtasks are linked by “part-of ” relationships. AO does not hold instances; instead it refers to Web-services responsible for supplying the DSS to data values. Web-service descriptions and the task knowledge are harmonized. Input and output arguments of functions that a Web-service implements correspond to class attributes of the task knowledge. Thus, the same attribute can be instantiated by several Webservices. The domain and task knowledge are interrelated by functional constraints showing what attribute of the domain knowledge takes its value as a function of the task output argument. The harmonization of AO and Web-service descriptions allows the Web-services to exchange information about their needs and possibilities in terms of the ontology vocabulary. Web-services describe their needs through a set of input arguments of the functions that these Web-services implement. Correspondingly, the possibilities are described through a set of output arguments of the functions. Relationships between the arguments and attributes of the classes representing the domain knowledge enable the Web-services to communicate about their arguments in terms of the domain knowledge. An ontology-based context [4, 5] is composed of knowledge relevant to the situation. Knowledge relevant to the situation is extracted from AO and integrated into an abstract context that is an ontology-based problem model or OOCN without values for model variables. The knowledge is extracted along with references to Web-services. The detailed description of the abstract context composing is presented in [6]. Data values are used to instantiate the abstract context. The instantiated abstract context is an operational context that is the problem model and values for the model variables. At the same time, this context is an OOCN to be interpreted as CSP. Different types of information sources are used for this instantiation (GIS, databases, sensors, web-sites, users). Thus, GIS is used as an information source for accessing the vector map of the situation region, available roads of the region, etc. A real-time picture of the situation, that is the operational context in a human readable form, is produced using different information sources, including GIS. The picture displays the physical map of the region where the roads and locations of the logistics members are indicated. This picture is presented to a decision maker and human members when their actions are requested. In the system approach (Fig. 1), two types of Web-services are distinguished: core Web-services and operational Web-services.

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The core Web-services are intended for user support and creating a situation general model, i.e., the abstract context. The set of core Web-services comprises: • MemberProfileService – responsible for creation, modification, and update of member profiles; access to the profiles; collecting information about the user; revealing user preferences • ManagementService manages Web-services to create the abstract context. This service operates with a service registry. In the registry the services MemberInteractionsService, AOAccessService, and AbstractContextService are registered • MemberInteractionsService – responsible for communicating with members. It arranges interactions between the system and members providing the members with messages, context-sensitive help, pictures of the current situations, results of CSP solving, and delivering information from the member to the system • AOAccessService – responsible for an access to the AO • AbstractContextService – responsible for creation, storage, and reuse of abstract contexts. The operational Web-services are intended for producing a real-time model of the current situation, i.e., operational context, and for problem solving.


The set of operational Web-services services comprises: • InformationSourceService is a set of Web-services responsible for interactions with information sources of different types and processing information provided by these sources. The following types of information sources are distinguished: sensors, databases, Web-sites, GIS, and users • ProblemSolvingService is a set of Web-services responsible for problem solving.

3 Operation Member Profile Profile of an operation member is given in Fig. 2. Competencies and preferences of the operation member are important for determining which member is capable of carrying out a specified task and, hence, can be chosen as a team member. Member competence is determined by capabilities, capacities, price-list, and speed. The operation member profile comprises: General Information, Operation Member Information, Request History, Operation Member Preferences. General Information Organization ID Organization Name Foundation Date Homepage

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Explicit Preferences Arrival Time Volume of Work Capability Restrictions Tacit Preferences Preferable Type of Accidents Preferable Properties

Fig. 2 Company competence profile

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The General Information part describes general information about an operation member organization. It contains the name of the organization, an organization identifier in the system, date of the organization foundation, and URL to the organization web page. Operation Member Information is a set of tuples describing information about the operation member. Each tuple contains the following properties: − Member Name: a name of an operation member − Location: current geographical location of a member; it can be taken into account for estimating rapidity and quality of request processing in a particular situation; this property is used by the GIS system for generating the map of the region with operation member, situation, and hospitals − Time: time zone of an operation member − List of Languages: represent languages for contacting an operation member − Rights: determine knowledge area which a member can access − Group: a member can be part of a group, based on its capabilities − Phone Number, E-mail: contact information − Operation Member Competencies includes the following properties: • Capabilities: determine types of operations that operation member can implement • Capacities: determine capacity of operation member (in case of evacuation how many people this operation member can evacuate) • Prices: determine evacuation member cost for implementation operation in case of implementation by private organization • Velocity: determine velocity of implementation operation by this operation member. Request History is also a set of tuples. Each tuple possesses the following properties: − Request: a request to a member − Context: is used to analyze performance of a member (other members can see solutions generated in particular situations) and to identify detectable member preferences − Operation member preferences: stores member preferences at the moment of request initiation. They contain a snapshot of all the properties of the category “Operation Member Preferences” − Operation member information: stores specific information about a member at the moment of request initiation. It contains a snapshot of all the properties of the category “operation member information”.


The Operation member preferences part consists of explicit preferences and tacit preferences. Explicit preferences describes member preferences that are manually introduced by a member. These preferences are used for choosing a member for a particular situation, and contain the member preferences for arrival time, volume of work, and capability constraints. The latter stores several capabilities and logical restrictions from a list of all the capabilities for the domain. Tacit preferences describe automatically detectable member preferences.

4 Case Study: GIS-Based Emergency Response System for Traffic Accident A group of people is traveling by vehicle. Suddenly this vehicle is involved in a traffic accident. The vehicle is supplied with a GPS (Global Positioning System), a smart sensor, and a transmitter. As soon as the sensor records the accident, it estimates how many people in the vehicle are injured and how much damage may have been caused to the vehicle. In addition, possible types of injury are assessed. The sensor determined that there had been a blow producing an ignition in the petrol tank. Likely, the four people need an emergency hospitalization. The location where the accident happened is positioned by the GPS. The information from the sensor and GPS is sent to an emergency response system by the transmitter. The emergency response system generates a real-time picture of the accident scene. The picture represents a map of the region with the region infrastructure, the accident location, and available resources to help traffic accident casualties. This picture is being submitted to a coordinator (a person in charge of the action on responding the traffic accident) along with a set of feasible action plans generated by the system for the available resources. The action plan for the traffic accident comprises a set of operation members selected by the system and a set of routes for different kinds of professionals to arrive at the accident location for their purposes. It should be noticed that professionals are considered to be resources as well. For the given scenario the system selects the following operation members: (1) emergency teams for emergency medical care and for transportation of the injured people to hospital(s), (2) fire fighters for ignition extinguishing, and (3) traffic police for the traffic accident investigation. For the selected operation members, the system generates a set of routes to be used for the above purposes. The routes are generated based on

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information about the types of vehicles available, the current weather conditions, the closed/open routes and the traffic jams. The coordinator chooses an action plan from the set proposed by the system, and corresponding actions are delivered to the members. If an operation member for some reason declines the plan, the system updates the current situation, regenerates the set of plans (taking into account that the above resource cannot participate), and submits the renewed set to the coordinator again. The plan updates are delivered to the resources. Figure 3 shows a scenario demonstrating interactions between different types of operation members. The emergency dispatcher enters a request about a traffic accident, through entering the points for accident location on the map, potential number of victims, and additional descriptions of the event. Based on this request an abstract context is built. Disaster

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The traffic accident scene (operational context) and the set of action plans are submitted to the decision maker. The decision maker chooses one solution (Fig. 4) from the generated set to be the decision. The solution presented in the figure is generated for four traffic accident victims, and eight emergency teams, eight firefighter brigades, and three police teams found in the region. Dotted lines in Fig. 4 depict routes to be used for transportation of the selected teams and brigades.


Fig. 4 Plan of actions for emergency teams and fire fighters

Fig. 5 Route for operation member on mobile phone screen

The interface of the system is Web-based, so regular Web browsers can be used for working with the system. The decision maker can see an interactive map and choose different parameters and criteria for problem solving. The vehicle drivers receive their assignments via Internet as well. They can see their routes using PDA or mobile phones (Fig. 5).

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The decision is delivered to the leaders of the emergency teams, fire fighters, and police teams, and to hospital administrations. They have access to the operational context through any Internet browser (a browser supported by a notebook, PDA, mobile phone, etc.).

5 Conclusion The developed service-oriented approach to context formation includes technologies of context management, ontology management, profiling, and Web-services. Context management technology enables the resources to be aware of real-world objects required in the current situation and typical problems being solved in situations of given types. Web-services provide the heterogeneous resources with a formal interface. Profiles allow for automating interaction between system and operation members. Profiles’ usage furnishes the operation members with the required information and the possibility to use competencies and preferences for the situation processing. Acknowledgments The research described in this paper is supported by grants from the following projects: grant No. 08–07–00264 of the Russian Foundation for Basic Research; grants No. 14.2.35 (research program “Mathematical Modelling and Intelligent Systems”) & No. 1.9 (research program “Fundamental Basics of Information Technologies and Computer Systems”) of the of the Russian Academy of Sciences (RAS).

References 1. 2. 3.

Jakobson G, Kokar MM, Lewis L, Buford J, and Matheus CJ (2005) Overview of situation management at SIMA 2005. In: Proceedings of the Workshop on Situation Management, Atlantic City, USA, pp 17–20 Scott P and Rogova G (2004) Crisis management in a data fusion synthetic task environment. In: Proceedings of the 7th Conference on Multisource Information Fusion Smirnov A, Pashkin M, Chilov C, and Levashova T (2003) Agent-based support of mass customization for corporate knowledge management. Eng Appl Artif Intell 16(4):349–364


4.

5. 6.

Griffiths J, Millard DE, Davis H, Michaelides DT, and Weal MJ (2002) Reconciling versioning and context in hypermedia structure servers. In: Proceedings of Metainformatics International Symposium, Esbjerg, pp 118–131; Denmark, http://eprints.ecs.soton.ac.uk/6829/01/mis02.pdf Robinson R (2000) Context management in mobile environments. Ph.D. Honours Thesis, School of Information Technology, University of Queensland, Australia; http://www.rickyrobinson.id.au/university/honours/thesis.doc Smirnov A, Kashevnik A, Levashova T, and Shilov N (2007) Context-driven information fusion for operational decision making in Humanitarian logistics. In: Popovich V, Korolenko K, and Schrenk M (eds): Proceedings of the third International Workshop – Information Fusion and Geographic Information System. Lecture Notes in Geoinformation and Cartography, Springer, St. Petersburg, Russia, pp 69–83

Immunocomputing for Geoinformation Fusion and Forecast

Alexander Tarakanov St. Petersburg Institute for Informatics and Automation of the RAS, 39, 14 Liniya, St. Petersburg, 199178, Russia, [email protected]

Abstract. Based on immunocomputing (IC), this paper proposes a new way for geoinformation fusion, spatio-temporal modeling, and forecast. The approach includes mathematically, rigorous mapping of high-dimensional spatio-temporal data into a scalar index, discrete tree transform (DTT) of the index values into states of cellular automata (CA), and identification of CA by IC. Numerical examples use official data of International Association for the Development of Freediving (AIDA), World Health Organization (WHO), as well as time series of Solar Influences Data Analysis Center (SIDC) and National Aeronautics and Space Administration (NASA). Anomaly index is also proposed using special the case of DTT. Recent results suggest that the IC approach outperforms (by training time and accuracy) state-of-the-art approaches of computational intelligence. Keywords: Immunocomputing; Geoinformation fusion; Spatiotemporal modeling; Forecast

1 Introduction Information fusion in a form of aggregated index proved to be rather important for situation assessment and decision making in geoinformation and cartography. The map of complex environmental conditions [1] can be treated as an example of such a geoinformation index. This main map of the ecological atlas had been developed for the administration of the big

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city using 11 maps of partial ecological indicators such as the pollution of air and water, soil conditions, etc. Another example provides such an extreme sport as freediving which includes eight disciplines and rigorous protocols to calculate the aggregated score (rating), according to the International Association for the Development of Freediving (AIDA) [2]. A simple geoinformation index is shown in Fig. 1. This index is based on the world and national records in the AIDA discipline “constant weight with fins” (CWT). In Fig. 2, this CWT index is compared with “gross national income per capita” registered by the World Health Organization (WHO) [3]. Although both indexes are rather artificial, the comparison reveals clear inverse correlation (0.89) between the free diving records and the common weal. This surprising result at least confirms once again the importance of information fusion in a geoinformation index. Based on immunocomputing (IC) [4], this paper proposes a new way for geoinformation fusion, spatio-temporal modeling, and forecast. The approach includes (1) mapping (fusion) of high-dimensional spatio-temporal (geoinformation) data into scalar index, (2) discrete tree transform (DTT) of the index values (time series) into states of cellular automata (CA), and (3) identification of CA by IC. Numerical examples use time series of Solar Influences Data Analysis Center (SIDC) and National Aeronautics and Space Administration (NASA). Most Beautiful Countries of Freedivers _WORLD RUS CZE FRA CRN JPN ITA SWE UK USA NZ GRE CRO CRO FIN NOR BEL AUS BRA DEN GER NED SUI

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2 Immunocomputing The above mentioned ecological map [1] (which can also be treated as the map of a geoinformation index) has been computed using the IC approach. This approach is based essentially on mathematical models extracted as an abstract of general principles of information processing by proteins and immune networks [4]. It is worth highlighting that the approach appears to be useful in brain research, especially for discovering deep (biomolecular) similarities in the functional organization of brain and immune system [5]–[9]. These similarities confirm, by the way, that the IC is actually “intelligent” [10]. The key model of the IC approach is the formal immune network (FIN). Let us give a general (informal) idea of FIN regarding information fusion. Let situation be represented by vector X with real-valued components (features): x1 ,..., x n . Note that the number n of components can be rather big. The vector X is treated by the IC as an “antigen.” Let three “antibodies” Y1 , Y2 , Y3 have been computed by the IC approach. These vectors are also n-dimensional. Consider three values of “binding energy” FIN 1 , FIN 2 , FIN 3 between the antibodies Y1 , Y2 , Y3 and

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any antigen X: FIN 1 ( X ) = Energy ( X , Y1 ) , FIN 2 ( X ) = Energy ( X , Y2 ) , FIN 3 ( X ) = Energy ( X , Y3 ) . Thus, any high-dimensional situation can be represented as a point of three-dimensional (3D) space of FIN . Consider m known (training or “immunizing”) situations X 1 ,..., X m . Let any training situation have known type c (integer class or real-valued “index”): c1 = c( X 1 ) ,…, c m = c( X m ) . For example, such an index can be assigned by expert(s) and/or computed by the IC approach. Then any unknown situation X is mapped to the space of FIN and recognized by the class of the nearest training point (“cell”) of the FIN . More formal IC algorithm of pattern recognition is as follows. Training { 1st stage training // form FIN { Get training patterns; DTT of the training patterns; //”antigen processing" Form training matrix; SVD of the training matrix; Store [q] singular values; // "binding energies" Store [q] right singular vectors; // "antibodies" Store [m] left singular vectors; // "cells" of FIN } 2nd stage training // compress data by "maturation" of FIN { Apoptosis; // kill unnecessary cells Immunization; // add cells to correct mistakes of Apoptosis } } Recognition { Get pattern; // antigen DTT of the pattern; // antigen processing Map the pattern to FIN; // using [q] antibodies Find nearest cell of FIN; Assign class of the nearest cell to the pattern; }

This approach has been successfully applied to the reconstruction, simulation and spatio–temporal forecast of hydrophysical fields [11]–[13]. In the geoinformation index, the IC algorithm can be modified as follows.

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Index Training { 1st stage training { Get [n] training signals; // "indicators" DTT of [n] training signals; Form training matrix [m x n] { While (i 5. For example, in this case for m = 5 and c = 6 (c = 12) we have ρ ≈ 3.2 ( ρ ≈ 3.8 ). Thus, using elliptic curves over vector finite fields, one can design the DS possessing significantly higher performance. Besides, the multiplication in the vector field GF( pυm ) is well suited to cheap parallelization while being implemented in hardware.

4 Conclusion To satisfy high performance requirements to DS algorithms oriented for the use in GIS the finite vector groups have been proposed as cryptographic primitive. Such groups are formed in finite vector space over the finite fields, while the vector multiplication operation is specially defined. In some particular cases the vector finite fields are formed. The structure of the vector groups is described in terms of the multi-dimension cyclicity.

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It has been shown that the vector groups possess the multi-dimension cyclic structure and formation of the vector fields corresponds to the case when the dimension of the cyclicity μ is equal to one. Fast digital signature algorithms based on vector groups with multi-dimension cyclicity ( μ = 2,3,4 ) have been proposed. It has been shown that the use of the vector finite groups to define ECs provides increasing performance of the ECbased DS algorithms.

Acknowledgement Paper supported by Russian Foundation for Basic Research grant No. 08-0700096-a

References 1. Pieprzyk J, Hardjono Th, and Seberry J (2003) Fundamentals of Computer Security. Springer, Berlin 2. Menezes AJ, Van Oorschot PC, and Vanstone SA (1997) Handbook of Applied Cryptography. CRC Press, Boca Raton, FL 3. Schnorr CP (1991) Efficient signature generation by smart cards. Cryptology 4:161–174 4. National Institute of Standards and Technology (1994), NIST FIPS PUB 186. Digital Signature Standard, U.S. Department of Commerce 5. GOST R 34.10-94 (1994) Russian Federation Standard. Information Technology. Cryptographic data Security. Produce and check procedures of Electronic Digital Signature based on Asymmetric Cryptographic Algorithm. Government Committee of the Russia for Standards (in Russian) 6. Koblitz N (1987) Elliptic curve cryptosystems. Math Comput 48:203–209 7. Miller V (1986) Use of elliptic curves in cryptography. Advances in cryptology: Proceedings of Crypto’85. LNCS, vol 218, Springer, Heidelberg, pp 417–426 8. Buchmann J (2003) Introduction to Cryptography. Springer, Berlin 9. ANSI X9.62 and FIPS 186-2 (1998). Elliptic curve signature algorithm 10. GOST R 34.10-2001 (2001) Russian Federation Standard. Information Technology. Cryptographic data Security. Produce and check procedures of Electronic Digital Signature. Government Committee of the Russia for Standards (in Russian). 11. Moldovyan NA and Moldovyanu PA (2008) New primitives for digital signature algorithms: Vector finite fields. Comput Sci J Moldova 12. Menezes AJ and Vanstone SA (1993) Elliptic curve cryptosystems and their implementation. J Cryptol 6:209–224

Multisignature Protocols and Problem of Simultaneous Signing a Package of Contracts

P. Moldovyanu, E. Dernova, A. Kostina, and N. Moldovyan Specialized Center of Program System “SPECTR”, Kantemirovskaya str. 10, St. Petersburg 197342, Russia, [email protected]

Abstract. Electronic messages authentication is an issue of significant importance for geographical information systems (GIS). In some cases the multisignature protocols can be effectively applied to solve efficiently special information authentication problems in GIS. New multisignature protocols based on the discrete logarithm problem in finite groups are introduced. The protocols are based on digital signature schemes over multiplicative groups of the ground finite fields, elliptic curves (EC), and vector groups, the last being a new cryptographic primitive. The protocols provide generation of the collective digital signature (DS) possessing internal integrity. Nobody participating in the protocol is able to form a valid collective DS that corresponds to reduced number of the signers. Besides, the collective DS of some special type can prove that a package of documents is signed by different sets of signers, and moreover the collective DS is formed simultaneously by all signers. Therefore the protocols solve the well-known problem of simultaneous signing a contract as well as they solve the more difficult problem of simultaneous signing a package of contracts. The paper considers applicability of the known DS standards to implement the proposed protocols. Keywords: Digital signature; Collective digital signature; Discrete logarithm problem; multisignature schemes; Public key; Finite group

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1 Introduction One of the important objectives of geographical information systems (GIS) is to produce the electronic reports on the on-line situation according to the data arrays retrieved from geographically remote information collection centers. Note that in most cases such arrays are formed on the basis of readings produced by several centers. As the result the following tasks arise: (1) generation of the collective digital signature (DS) relating to the single message, (2) simultaneous signing an electronic document, and (3) simultaneous signing a packet of electronic messages. The second problem is well known in the area of cryptographic protocols as the problem of simultaneous signing a contract. The third problem can be considered as some advanced variant of the second one. The listed problems can be solved with respectively designed multisignature (MS) protocols. Such protocols appears to be sufficiently useful in solving some types of electronic documents authentication in GIS. There are known different constructions of the MS schemes [1, 2]. However only a few of them provides the property of internal integrity that means no manipulation with the signature is possible during and after the signature generation. The internal integrity property is provided by the approach proposed in [3], which can be applied to develop MS protocols based on different types of the difficult computational problems. That approach is used in this paper to develop some new MS schemes including the schemes providing efficient solution of the problem of simultaneous signing a package of contracts by different sets of signers. The proposed protocols have the prospect for application in GIS and in other different large information systems.

2 Multisignature Protocols with Randomized Signature Algorithms 2.1 Collective Digital Scheme Based on Dificulty of Finding Roots Modulo Prime In this section we surveyed the approach to design the MS schemes based on randomized DS algorithms, which has been proposed in [3]. That approach uses the collective process of the formation of some common random parameter E that depends on the message to be signed and on some set of random values generated by each of the signers participating in the MS protocol. The parameter E is the first part of the DS. Then the parameter E is used individually by each signer to compute his share in

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the collective DS. The individual actions of the signers are performed in correspondence with some randomized DS algorithm put into the base of the protocol. Then some digest S of all shares of the signers is computed as the second part of the collective DS (E, S ) . The paper [3] proposed the MS scheme based on difficulty of finding the kth roots modulo large prime p such that k 2 p − 1 , where k is a prime having the bit size k ≥ 160 bits. The scheme works as follows. Suppose the jth user owns the private key X j < p and the public key Y j = X kj mod p , where j = 1,2,..., n. Suppose some subset of m users is to sign a message M with some single DS called collective DS. The following protocol solves the problem. 1. Each user generates a random value t i < p (it is a onepad secret key) and calculates the value Ri = t ik mod p , where i = α 1 , α 2 ,..., α m ( α 1 , α 2 ,..., α m ∈ {1,2,..., n} ). 2. The common randomization value R is computed: R = Rα1 Rα 2 ...Rα m mod p.

3. The first part E of the collective DS ( E , S ) is computed using some specified hash function ƒ: E = f (R, M ) . 4. Using the common value R and individual one-pad secret key t i each of the users computes its share in the collective DS: S i = X iE t i mod p , i = α 1 , α 2 ,..., α m .

5. Compute the second part S of the collective DS: S = Sα1 Sα 21 ...Sα m mod p.

The collective DS verification is performed as follows. 1. Compute the collective public key Y: Y = Yα1 Yα 21 ...Yα m mod p.

2. Using the signature (E S) compute value R *: R * = Y E S k mod p.

3. Compute E * = f (R * , M ) .

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4. Compare values E * and E. If E * = E, then the signature is valid. Otherwise the signature is rejected. In this protocol none of the signers generates his individual signature; he generates only its share in the collective DS that corresponds exactly to the set of m user presented by numbers {α 1 , α 2 ,..., α m }. Besides it is computationally difficult to manipulate with shares S i , i ∈ {α 1 , α 2 ,..., α m } , and compose another collective DS, relating to some different set of users. This fact imparts on the collective DS the property of the internal integrity, therefore the proposed protocols efficiently solve the problem of signing a contract simultaneously [4]. Note that the MS protocols proposed in [1] are not able to solve this problem without the help of some trusted party participating in the protocol. Collective DS protocols possess the following advantages: 1. The digital signature length is fixed (it does not depend on number of signers and equals the length of individual DS provided by the underlying DS algorithm) 2. The standard public key infrastracture (PKI) is used 3. The protocol can be implemented using DS algorithms specified by some of the existing standards. Among the known protocols should be indicated the group digital signature protocol. It sounds like a collective DS, but it is a different protocol that means that anyone from a group of authorized users can sign a document on behalf of the group and only the leader of the group is able to learn the users that have signed the document. Thus, the collective DS differs from the group DS. It seems that collective DS is significantly more useful for practical informatics. 2.2 Security of the Collective Signature Protocols Security of the collective DS protocols based on randomized DS algorithms has been considered in [3] with the example of the collective DS implementation using the Russian DS standard GOST R 34.10–94 [5] regarding the following two types of general attacks. The attack of the first type corresponds to forgery of the collective DS. The second type of attack corresponds to calculation of the secret key of one of the signers that share a collective DS. In the first attack it is assumed that m − 1 legitimate signers attempt to create a collective DS corresponding to m signers. In the second attack it is assumed that m − 1 signers that share some collective DS (R, S ) with the mth signer are trying to compute the secret key of the


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mth signer. It has been proved [3] that any successful method to perform any of the attacks can break the underlying DS algorithm. Thus, security of the collective DS protocol has been reduced to security of the underlying algorithm. Such an approach to formal security proof and the technique used is applicable to a variety of the collective DS protocols based on randomized DS algorithms. Due to the possibility to provide the reduction proof of the protocol security one can compose different secure protocols using secure DS algorithms. In this connection it is prospective to implement protocols using DS standards or provably secure DS algorithms. Unfortunately only a few DS standards allow to implement the collective DS protocols on their base. Besides, the example of the GOST R 34.10–94 mentioned above, the GOST R 34.10–2001 [6] is also suitable for such a purpose. The protocol can also be implemented with Schnorr’s DS scheme [7] which is one of provably secure ones. The use of Schnorr's DS algorithms provides construction of the provably secure collective DS protocol. In Schnorr’s DS scheme there is used the prime modulus p such that p − 1 contains a large prime factor q , the element g that is generator of the q order subgroup in Ζ p , and public key y = g x mod p , where x is the secret key. Suppose the m users should sign some message M. The collective DS protocol looks as follows. 1. Each of the users generates his individual random value t i , computes Ri = g ti mod p , and presents the value Ri to each of the users. 2. The common randomization parameter is computed as the product R = R1 R2 ...Rm mod p.

3. The first element of the collective DS E = FH (M || R ) is computed, where M is the message to be signed. 4. Each of the users computes his share in the second element of the collective DS Si = ti + xi E mod q.

5. The second element S of the collective DS (R, S) is computed: S = S1 + S 2 + ... + S m mod q.

The signature verification is performed exactly as in Schnorr’s DS algorithm [7]. This protocol works correctly. Indeed,

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−E

m

m

i =1

i =1

m

∑ ti

≡ y g i =1 g −E

m

E

∑ xi i =1

m

∑ ti

≡ y g i =1 y E ≡ −E

≡ ∏ Ri ≡ ∏ Ri mod p = R.

2.3 The Collective DS Protocol Implementation Using GOST R 34.10–2001 Standard GOST R 34.10 2001 [6] specifies the DS algorithms based on the ECs defined over the ground field GP( p ) with the following equation y 2 = x 3 + ax + b mod p , where a, b ∈ GP( p ) and y and x are coordinates of the point of the EC. For details of the EC cryptography see [8, 9]. Using GOST R 34.10 2001 the collective DS generation is performed in the following way. 1. Each ith signer selects at random a value t i and computes the EC point C i = k i P , where P is the q order point of the EC ( q is a prime). 2. The common randomization point C = C1 + C 2 + ... + C m and the randomization value RC = mod x q are computed, where xC is the abscissa of the EC point C. The value R is the first part of the collective DS. 3. Each user computes his share in the composite DS as follows Si = ( Rdi + ki e ) mod q, where d i < q is the secret key of the ith user, e = H mod q , is the hash function value. m

4. The second part of the signature is S = ∑ S i . The full signature is

(R, S ).

i =1

The verification of the signature (R, S ) is performed as follows. m

1. Compute the collective public key as the point Q = ∑ Qi , where the i =1

EC point Qi is the ith signer public key (Qi = d i P ) , i = 1,2,..., m . 2. Compute the EC point C * = ((Se −1 )mod q )P + ((q − R )e −1 mod q )Q .

3. Compute the value R * = xC * mod q and compare R * and R . If R * = R , then the collective DS is valid.


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3 Multisignature Protocols for Simultaneous Signing a Package of Contracts The protocols presented above can be used to solve efficiently the problem of simultaneous signing a contract. However they do not provide an efficient solution of the problem of simultaneous signing a package of contracts. The last problem considers the cases when the first subset of some signers should sign the first document, the second subset should sign the second document, the third subset should sign the third document, and so on. In addition all documents should be signed simultaneously. Since in such a problem we have different documents and different hash functions corresponding to the respective documents, the described protocols are not applicable to solve the problem. However using the idea of the collective DS protocols it is possible to propose the analogous MS protocols that provide the solution. Two examples of such protocols, called the composite protocols, are described below. In the first protocol the collective public key dependent on the hash function is used. In the second protocol the documents are arranged in a special way with the parameters Ri to form the argument of the hash function. Suppose the parameters p , q , and g as well as secret x and public key y = g x mod p are specified as in Schnorr’s DS scheme (see Sect. 2.2). Suppose the m users should sign m different messages M i , i = 1,2,..., m . The first composite DS protocol looks as follows. 1. Each ith signer selects at random the value t i and computes the randomization factor Ri = g ti mod p , where i = 1,2,..., m. 2. The common randomization factor R is computed: R = R1 ⋅ R2 ⋅ R3 ⋅ ... ⋅ Rm mod p .

3. The first element e of the composite DS is computed using the formula e = f (R ) , where f is some compression function, for example, f (R ) = R mod p . 4. Each of the users computes his share in the composite DS as follows S i = t i − ehi xi mod q ,

where xi is the secret key of the ith user. 5. The second element S of the composite DS is computed as the following sum


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S = S1 + S 2 + ... + S m mod q .

The verification procedure of the composite DS is as follows. m

1. Compute the composite public key y : y = ∏ y ihi mod p , where hi is i =1

the hash function value computed from the ith document and y i = g xi is the public key of the ith signer. 2. Compute the value R * = y e g E mod p and e * = f (R * ) .

3. Compare e and e *. If e * = e , then the composite DS is valid. 4. The correctness of the composite DS is proved as follows: m

m

R =y g =y g *

e

S

e

∑ Si i =1

=y g e

∑ (ti −ehi xi ) i =1

⎞ ⎛ m ⎞⎛ = y ⎜⎜ ∏ g ti ⎟⎟⎜⎜ ∏ g hi xi ⎟⎟ ⎝ i =1 ⎠⎝ ⎠ e

−e

=

= y e Ry −e = R(mod p ) ⇒ e * = f (R * ) = f (R ) = e.

The second composite DS protocol is specified as follows. 1. Each i–th signer computes the randomization factor Ri = g ti mod p , where t i a random value, i = 1,2,..., m . m

2. Compute the common randomization factor R = ∏ Rm mod p . i =1

3. The first element e of the composite DS is computed using some specified hash function FH as follows: e = FH ( M 1 || y1 || M 2 || y2 || ... || M m || ym || R ) .

4. Each of the users computes his share in the composite DS using the formula Si = ti + exi mod q,

where xi is the secret key of the ith user. 5. Compute the second element of the composite DS: m

S = ∑ S m mod q . i =1

The composite DS verification is performed as the following steps.


339

m

1. Compute the public key y = ∏ y i mod p , where y i = g xi is the pubi =1

lic key of the ith signer, i = 1,2,..., m. 2. Compute the values R * and e * 3. R * = y − e g S mod p and e * = FH (M 1 || y1 || M 2 || y 2 || ... || M m || Ym || R * ) .

If e * = e , then the composite DS is valid. The correctness of the last composite DS is proved as follows: ∑

⎛ ⎞ ⎜ ⎟ ⎜ t + ex ⎟ ⎜ ⎟ i =1 ⎜ ⎟ ⎜ ⎟ i⎠ ⎝i m

m

∑

⎛ m t = y e ⎜⎜ ∏ g i R* = y e g S = y e g i = y e g ⎜ i =1 ⎝ e −e * = y Ry = R ( mod p ) ⇒ e = e. i =1

S

x ⎞⎛ ⎟⎜ i ⎟⎜ ∏ g ⎟⎜ ⎠⎝

e

⎞ ⎟ = ⎟ ⎟ ⎠

Both of the composite DS protocols described above can be implemented using the finite groups of points of the elliptic curves, but none of the DS algorithms specified by the existing DS standards can be used as the base DS algorithms in the composite DS protocols, since all the standards specify the signature generation and verification procedures accepting one hash function value. However, using the ideas underlying the proposed composite DS protocols, one can propose other different implementation variants. As in the case of collective DS protocols, the last two protocols form the signature possessing the internal integrity. If the process is somehow stopped then no valid signature is formed. If the process is performed completely, then all signers have signed the whole package of documents M 1 , M 2 ,..., M m . Since the composite signature is formed simultaneously, it is possible to apply the protocol for solving the problem of simultaneous signing a package of contracts. Indeed, suppose the signers 1 to α * have to sign the contract M, the signers α * to α ** have to sign the contract M *, and the signers α ** + 1 to m have to sign the contract M **. Assigning in the composite DS protocols H i = FH ( M ) for i = 1,2,..., α * , H = F ( M *) for i = α * + 1, α * + 2,..., α **, and H i = FH ( M ** ) for i = α ** + 1, α ** + 2,..., m , correspondingly, it is possible to sign simultaneously the contracts M , M *, and M ** providing high security of the simultaneous formation of the DS. i

H

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4 Conclusion We have presented several new MS schemes providing formation of the collective and composite DS and shown that the protocols solve efficiently the problem of simultaneous signing a contract by an arbitrary number of signers (collective DS), as well as the problem of simultaneous signing a package of contracts by arbitrary subsets of signers assigned to each of the contracts. Some variants of the protocols are implemented using the computations modulo large prime number and some others are implemented using computations on EC. There are no limitations to the implemention of the protocols using any other finite group, provided the group contains a subgroup of the sufficiently large prime order. To satisfy high performance requirements to MS protocols oriented for the use in GIS the finite vector groups [10] are prospective. The use of the finite groups of the vectors defined over the finite fields ( GF ( p ) or GF ( p n ) ) allows to implement fast DS algorithms based on two difficult problems: finding discrete logarithm and finding large prime roots [10]. The vector finite groups (VFG) are very interesting for such implementations due to the following two facts: (1) for the given group order the group operation of the VFG is computationally less expensive that in other groups used in cryptographic algorithms; and (2) the group operation of the VFG allows efficient parallelization.

Acknowledgement Paper supported by Russian Foundation for Basic Research grant No. 08–07–90100–Mola.

References 1. Boldyreva A (2003) Efficient Threshold Signature, Multisignature and Blind Signature Shemes Based on the Gap–Diffi–Hellman–Group Signature Sheme. vol 2139, Springer, Heidelberg, pp 31–46 2. Min-Shiang Hwang and Cheng-Chi Lee (2005) Research issues and challenges for multiple digital signatures. Int J of Network Security 1:1–7 3. Minh NH, Moldovyan NA, and Minh NL (2008) New multisignature protocol based on randomized signature algorithms. In: 2008 IEEE International Conference on Research, Innovation and Vision for the Future in computing & Communication Technologies. University of Science. Vietnam National University, Ho Chi Minh City 4. Schneier B (1996) Applied Cryptography, 2nd Edition. Wiley, New York


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5. GOST R 34.10–94 (1994) Russian Federation Standard. Information Technology. Cryptographic data Security. Produce and check procedures of Electronic Digital Signature based on Asymmetric Cryptographic Algorithm. Government Committee of the Russia for Standards (in Russian) 6. GOST R 34.10–2001 (2001) Russian Federation Standard. Information Technology. Cryptographic data Security. Produce and check procedures of Electronic Digital Signature. Government Committee of the Russia for Standards (in Russian) 7. Schnorr CP (1991) Efficient signature generation by smart cards. Cryptology 4:161–174 8. Koblitz N (1987) Elliptic curve cryptosystems. Math Comput Adv 48:203–209 9. Miller V (1986) Use of elliptic curves in cryptography, Advances in cryptology. In: Proceedings of Crypto’85, vol 218, Springer, Heidelberg, pp 417–426 10. Moldovyan NA and Moldovyan AA (2009) Vector Finite Groups as Primitives for Fast Digital Signature Algorithms Springer. In: 4th Int. Workshop IF&GIS’09 Russia, St. Petersburg, Springer, Heidelberg

Integrated Usage of Data Mining Methods for Malware Detection

Dmitriy Komashinskiy and Igor Kotenko St. Petersburg Institute for Informatics and Automation (SPIIRAS), 39, 14 Linia, St. Petersburg, Russia, [email protected]

Abstract. The problem of counteracting malicious software (malware) remains a real one in all computer systems, including Geographical Information Systems (GIS), despite the obv ious successes of antivirus vendors in technologies aimed at early recognition of malware propagation, code analysis, and malware rapid updating of databases. The basic issue of that problem is the quality of heuristic detection methods. The goal of these methods is to provide recognition of unknown malware samples; therefore heuristic detection is the last defense line of any critical object in IT infrastructure. The paper is devoted to the application of data mining methods to heuristic detector development. The offered approach differs from existing ones by cyclic interactive covert processing of behavioral information, and integrated use of different methods of data mining for various classes of malware. The paper discusses research into how a family of different data mining methods based on Bayes approach, decision trees and neural networks were implemented and investigated. The paper proposes a general integrated approach to realization of malware detection methods. Keywords: Data mining; Malware; Behavior-based malware detection

V.V. Popovich et al. (eds.), Information Fusion and Geographic Information Systems, Lecture Notes in Geoinformation and Cartography. DOI: 10.1007/978-3-642-00304-2_24, © Springer-Verlag Berlin Heidelberg 2009

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D. Komashinskiy, I. Kotenko

1 Introduction In spite of great efforts made by research and development community and commercial organizations, the defense problem of personal and business resources information against malware persists. It is important to note that the problem of malware protection is extremely prevalent in distributed GIS. Mechanisms of malware protection in GIS should be a part of the common security services and provide protection of the information and GIS functioning processes. In accordance with the estimation of some researchers, by the middle of 2008 about 90% of personal computers, regularly used to access the internet, were exposed to malware impact. That is a real challenge, so there exists a need to further develope the technical decisions directed to the malware recognition in its active phase – when it is run on an attacked host. The topicality of recognition of malware running on the host is determined by the following facts: the malicious functionality focus is moving towards speeding-up and hiding; the invariance of its penetration types is growing (penetration invariance); the duration of its hidden phase is extending; static detection techniques are not sufficient to provide for a reliable defense against new malicious examples. Propriety of the detection of running malware (in its active phase) is obvious, because the malware is more understandable – it must perform its own main malicious functions. Moreover, it should be taken into account that, from the technical point of view, the realization of behavior polymorphism is a significantly more complex task than the realization of the structural one. However, this aspect should also be accounted for in the prospective antimalware techniques. The paper outlines an approach to detection of malicious software based on hidden gathering of information about running applications and their processing via data mining-based techniques. The approach proposed differs from the existing techniques by cyclic interactive hidden processing of behavior data as well as by flexible integrated usage of data mining techniques for each malware type taking into account the admissible compromise between the main requirements for the malware detection process. The paper is structured as follows. Second section is dedicated to related work. Third section describes the malware detection task, its main typical requirements and restrictions as well as recites some peculiarities related to the malware detection based on data mining methods. Fourth section introduces behavior-based detection approach investigated in our research. Fifth section specifies the implementation details of the detection simulation software used and describes the performed experiments and current research results. Conclusion generalizes the main results of our work and indicates the prospective research directions.


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2 Related Work The question about applicability of data mining (DM) techniques to malware detection arose when the research community understood the need to reinforce, by heuristic methods, the existed malware detection techniques. DM is not a panacea, but these methods bring systematic character into the process of malicious software detection in accordance with experts-defined rules. By now a great number of works related to malware detection have been published. Let us analyze some more interesting ones. The paper considers a detection model as one or several learned classifiers that work together to get the optimal result in accordance with determined requirements for the malware detection process. M. Schultz et al. [6] presented one of the first published research results devoted to usage of classifiers (as a part of DM knowledge area) for malware detection. The paper [6] considered issues no longer current due to serious progress in binary protection tools based on static analysis. However, it provided a good foundation for DM-based detection for many researchers and demonstrated all the power of the DM-based approach. This paper is dedicated to the detection of new (unknown) malicious programs propagated via e-mail logical channels in Portable Executable 32-bit (PE32) attachments [3]. The basic assumption used is that the structure of PE32 files and the contained code were not subjected to intentional distortion to make their analysis difficult. Learning and test sets include executable binaries of two predefined classes: benign and malicious. Thus, a dichotomy task is investigated. To compare the effectiveness of the proposed DM-based detection approach with a traditional signature-based one, a specially developed automatic signature generator is used. It fills the storage of designed known binary signatures with ones from a malicious initial file subset. The quality of the formed detection models was estimated as the traditional calculation of false positives and false negatives. The feature extraction process for instantiation of the DM-based detection models was organized as follows: • From each binary file of learning/test collection a binary profile (a set of extracted features) was extracted with specially chosen tools – parser of 32-bit portable executable format, null-terminated string-extraction tool and transformation tool of binary content to visible symbol sequence. Thus, at least three types of features have been used: some signs of PE32 file (for example, GUI type), strings (which are usually used for imports/exports descriptions), and code fragments. These sets are typical for the static DM-analysis of PE32 binaries, but any packer/protector

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•

• • •


tool can hide this information layer by changing it to the specific values of these features. Data from binary profiles were used for the feature selection process, and each implemented type of the DM-detector applied its own data subset (for example, RIPPER-based classifier used PE32-specific data, statistic-based classifiers used string and binary features). The following classifiers were used for DM-based detectors: RIPPER classifier is rule-oriented and its output decision-making model appears to be a rule that sets well-defined result of classification in accordance with the values of features used during classifier learning; Statistical naive Bayes (NB) classifier calculates a probability of a file virulence rating for each class predefined at the learning stage; Multiplicative NB classifier is an artificial construction based on the combination of detection models using the separated feature groups. This approach was directed to investigate the necessary resource minimization at the expense of separating the global classification task into the simpler subtasks with further results combining.

J. Wang, et al. [8] suggested the DM-based detection model, also using the features generated by the PE32 executable binaries. The process of the detection model design, instantiation, and validation can be represented as follows: preparation of two file sets with intentional malware and benign binaries; separation of the whole available file sets into two parts: an initial subset (for the classifier learning and cross-tests) and a test subset (for the validation phase); extraction from each file of an initial binary profile (content of code section from file); feature extraction (any feature is just a byte sequence consisting of CPU instruction opcode and some following bytes); feature selection; classifier learning; detection model evaluation. Selection of the features valuable for the learning task was provided by the information gain function calculation for each extracted feature. After prioritizing the feature list the valuable signs are at the top. Validation phase of the constructed malware detectors was carried out through calculation of FP and FN for initial (cross-check) and test (model evaluation) file sets. Detection models were constructed by the naive Bayes and decision tree classifiers. Decision tree exceeded NB according to FP and FN parameters. The most important fact not related to the results of a comparison between the methods used is that the extension of the feature size (the usage of a byte sequence instead of 1-byte opcode) has a positive influence on the detection accuracy. B. Zhang, et al. [9] considered the design of the DM-based detection model based on support vector machine (SVM) classifier. They proposed


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to use the behavior-based features collected during the execution of the analyzed binary files. The issue related to protecting against the security threats caused by the real malware samples executing on a victim host was solved by the network environment isolation. On the whole, as the authors showed, a practical usage of environment isolation may be organized by the specially prepared analysis infrastructure with wide involvement of virtualization technologies and additional security policies. The application of such a non-trivial approach was caused by the above mentioned difficulties in analyzing the specially obfuscated structure and code being a typical countermeasure against static analysis. Some special assumptions were marked before the start of experiment: • All malicious programs function via Win32 API calls; in accordance with the current development practice this assumption is pretty correct for the user-level applications and components; • When a malicious application is running, it captures the attacked host and prevents the running of any other malicious example. That is a basic item that provides the clarity of experimentally gathered data; • Win32 API may be effectively intercepted. That is indeed true, and the main message of that point is a need to somehow intercept the program actions to organize a behavior-based analysis. Each intercepted function had its own personal identifier. The input operands and the returned values of these functions were not taken into account. Thus, processed execution trace could be considered as a chronological list of the called function identifiers. Feature extraction process was organized as a gathering of all those existing in the execution trace chains with fixed length. To select the most valuable features from the extracted one, the authors calculated the Hamming distance between the malicious trace fragments (features) and intentionally benign ones.

3 Peculiarities of DM-Based Malware Detection Ultimately, the result of malware detection function can be considered as the Boolean positive (1/true/malicious) or negative (0/false/benign) variable. It is evident that the restricted isolated database about malicious examples (signature base or some type of heuristic detector initialization data) does not provide the successful detection of new malicious examples. Therefore, it is necessary to find more effective and flexible detection techniques.

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Apparently, a more effective way from the security point of view is the detection of a code sample without its execution, because in such a situation the hosts with antivirus software cannot be infected (exposed to another malicious influence). Such type of detection is known as static. There exist powerful means to hide the original code or make it unreadable (for instance, binary packers and protectors, script code obfuscators, etc.). And, as a result, these techniques make the static analysis difficult. The proper answer to these difficulties is the “on-fly” detection techniques with this simple rule: make checks when the application is running. There are many different examples of this kind: memory scan of the running processes, behaviorbased detectors, some kinds of emulators, etc. Of course, that way is more dangerous because a host used for analysis may be infected. This approach in general is only applicable to the environment restricted and isolated by technical or organizational means. The typical antivirus software has three basic functions [1]: malware detection, malware identification, and host disinfection. Detection function is responsible for making the decision about the threat of the analyzed object. In the general case, it just finds an answer to the question “Is the code (code container) malicious?” There is no detection approach granting an absolutely correct result for all existing and future malware examples. The reason lies in the active role of the IT criminal agents who try to use novel technologies and software environments in order to carry out their business. Usually the identification function takes into its own input the results of the detection function and is responsible for exposing the precise type/name of the detected malicious program. Output results of that function are used for disinfection. It realizes actions on detected malware removal and cleans all side-effects. Disinfection function cannot organize its own work correctly if identification fails due to the impossibility of removing all detected malware-specific effects from the attacked host. The detection and disinfection functions are realized in deterministic as well as in more generalized (heuristic) ways. As a rule, deterministic approaches are directed to counteract against the known malware examples (signature scanning). Opposed to this, the heuristic techniques allow the detection and removal of new malicious programs and are based on the generalization of the experts’/analysts’ experience. Any malware detection approach must satisfy the following main general requirements: detection accuracy, efficiency (productivity, performance), and host resources consumption (Fig. 1). As one can see, any attempt to enhance one of these parameters leads to decrease of the other two.


Initial State: S0 = {A0, R0, P0}; Optimization attempt: O target: A to max; keep P. S1 = O(S0) = {A1, R1, P1}; As result: A1 better than A0; P1 is close to P0; R1 worse than R0.

Accuracy

A1 A0 R1

S1 O

S0 R0

Resources

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P1 P0 Performance

Fig. 1 Quality triangle of malware detection function

The detection accuracy requirement is determined as a conformity of the detection model with the predefined error indicators of false positives (FP, detection of benign object as malicious) and false negatives (FN, detection of malicious object as benign). Basically, DM is applicable to heuristic malware detection approaches, so values of FP/FN rates may be less strict in comparison with other approaches (for example, ideally the case signaturebased detection should have the following numbers: FP = 0, FN = 0). Using the related works [1, 6, 8, 9] and the initial results obtained here, the following potentially reachable values FP = 0.15, FN = 0.05 may be indicated. In other words, the target detection model can make mistakes due to its specificity. However, within certain limits, the FP detection error is more preferable (as minimal evil). So, the DM-based detection model should be “paranoiac” to a certain degree. The performance requirement sets the allowable time of decision-making. In respect of techniques to be used in the proposed approach, it should be noted that the dynamical detection model using the behavior-based information can have no precise performance indicators. Due to its specificity, the positive result of detection is possible only when the analyzed object (code container) has performed some set of the operations (in our case it is a set of malicious functional blocks specific for the detection model). Certainly, if the DM-based detection model uses features that can be extracted and selected without running the analyzed object (static approach), the performance requirements may be set accurately, without taking into account the restrictions mentioned above. The common target of the performance requirement is to make the DM-based detection as fast as possible. The requirement of host resources consumption determines the amount of random access memory (RAM) and the memory access quantity needed to perform calculations related to the detection model. That requirement depends on the calculation complexity and data volume used. Usually, the

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attempt to improve the indicator of resources consumption stipulates the performance decrease. Taking into account the characteristics of modern computers and high resources consumption by the DM-based detection models, let us note that this requirement is secondary and bring into focus the minimization of resources consumption by the search for local optimal detection models for particular types of malicious objects. In other words, the search for abstract ineffective universal detection models will be omitted, and major effort will be concentrated on finding the practice-oriented particular effective models that may be combined in one theoretical basis. The malware detection result is an object class, the detection model refers the target object on the basis of the processing rules used on the computing infrastructure (simulation testbed) used. These processing rules include the rules of feature extraction, feature selection, and processing (classification) of significant features. The task of satisfaction of the common requirements described above can be expressed by the requirements for data processing rules used by the detection models to be constructed on the basis of the DM methods. When forming the detection model it is important to perform the theoretical justification and practical evaluation of possible variants of processing rules. With a glance at a strong dependency of the common requirements on the quality of processing rules, it is suggested to fulfill the additional estimation of those rules using the following factors: computational complexity of feature extraction rules; number of extracted features; computational complexity of feature selection rules; number of selected features; memory volume needed to store features; computational complexity of classification rules (in the common case of chosen DM technique). Taking into account these values at early stages of model preparation, it seems possible to minimize the time of the detection model development and to optimize a degree of conformity in the common requirements.

4 Investigated Behavior-Based Malware Detection Approach The offered approach is based on integrated combined use of the DM techniques. It is obvious that the detection of malicious software can be based on two main approaches: misuse detection – revealing the obviously dangerous static and dynamical behavioral signs (as a rule, they vary for different malware classes), and anomaly detection – searching the differences from


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typical signs of obviously secure applications. The peculiarities of these approaches stipulate the need to do the research devoted to integration of different detection methods that use a reasonable compromise between the common requirements: (1) detection accuracy – minimal errors in false positives and false negatives; (2) efficiency (productivity, performance); (3) effective host resources consumption. Moreover, it is necessary to have the accurate estimations of applicability of the rules of feature extraction, feature selection, and processing (classification). In addition, it is necessary to consider two specific requirements: the stability of detection functionality and the secrecy of data gathering. The second requirement is necessary as there exist many anti-monitoring and anti-emulation techniques [4] allowing the malware to recognize the fact of being monitored. Use of the classification methods assumes carrying out the supervised learning of the chosen classifier with the subsequent use of adjusted parameters (weights). Features are picked out in the process of the classifier training by the example containing the applications carried to target classes (for example, malware/benign). The space of features is multidimensional and is defined by the selected features quantity. The detection model represents the function, defined on the space of features, optimally relating the investigated object to one of target classes. The classification models used in the considered approach are based on the following groups of methods [2]: statistical group – the NB classifier (assumes the independence of selected features) and its specializations (decrease the assumption about independence of selected features) are used; inductive group – decision tree technique is used; the classifiers based on the sets separation; in the current research phase, the multilayer perceptron classifier was chosen. The gathering of initial data in our approach is based on the low-level operating system functions (native API) monitoring. It allows getting the chronologically correct sequence of events on the usage of the critical system resources by the analyzed applications. The monitoring secrecy is based on the intended modification of operating system kernel structures that are inaccessible for the applications functioning in the user mode. Thereby, the running applications are deprived of an opportunity to determine that they are being monitored using evident signs. Requirements related to the performance and resources usage generally depend on the quality of event interception and analysis modules (absence of bugs, usage of optimal algorithms). Moreover, the performance requirement is indirectly defined by peculiarities of the classifiers used (for example, by possibility of fast re-learning).

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5 Experiments 5.1 Tools Used The tools used for data gathering and the results evaluation were constructed on the basis of Windows XP (NT5.1), being now one of the most popular from the point of view of typical users and, as a consequence, from the point of view of malefactors. Gathering of behavioral information is based on the usage of program interceptors of native API functions [5]. To form the initial dataset and test data for validation of the models generated by classifiers, we used the set of malware from [7] and typical benign applications from the operating system and commonly used distributives. For learning, cross-checking, control checking, and results visualization the Weka Classifier software tool was used. Input data for that tool are presented in ARFF (attribute-relation file format). The typical data description contains two main parts: (1) definitions, where all used features are described and typified (Fig. 2); and (2) input data, represented as 2D array with features’ values for each input sample (Fig. 3). For validation checking of the prepared detection models, at least two such input data blocks should be prepared: for learning file set and for test file set. The applications necessary to get the call traces were executed in an isolated computing environment under maximally privileged credentials (local administrator). To carry out experiments the special malware set was used, including malicious applications with evident functionality containing file search/read hierarchical cycle (for example, e-mail worms collect the list of possible recipients of new malware copies via text files), file infection, and automatic start after reboot/user logon.

Fig. 2 Input data description, definition part


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Fig. 3 Input data description, input data set

5.2 Feature Extraction and Selection The base data for feature extraction/selection process is a collected call trace of the analyzed application. Below is the typical example of the raw trace for malicious sample run on isolated host (it is the creation of functional copy in the user directory): NtCreateFile(+24C.6C,n40110080,o"\??\C:\DOCUMENTS AND SETTINGS\ALL USERS\...\EQYY.EXE",i0.2,l,n20,n0,n5,n64,p,n0) NtQueryVolumeInformationFile(!24C.68="\??\C:\SAMPLE\TANATOS.EXE",i0.8,p12E88C ,n8,n4) NtQueryVolumeInformationFile(!24C.6C="\??\C:\DOCUMENTS AND SETTINGS\ALLUSERS\...\EQYY.EXE",i0.8,p12E88C,n8,n4) NtSetInformationFile(!24C.6C="\??\C:\DOCUMENTS AND SETTINGS\ALL USERS\...\EQYY.EXE",i0.0,p12ED2C,n8,n14) NtWriteFile(!24C.6C="\??\C:\DOCUMENTS AND SETTINGS\ALL USERS\...\EQYY.EXE",p,p,p,i0.10000,p930000,n10000,l,d ) NtWriteFile(!24C.6C="\??\C:\DOCUMENTS AND SETTINGS\ALL USERS\...EQYY.EXE",p,p,p,i0.1A00,p940000,n1A00,l,d) NtSetInformationFile(!24C.6C="\??\C:\DOCUMENTS AND SETTINGS\ALL USERS\...\EQYY.EXE",i0.0,p12F0B4,n28,n4) NtClose(-24C.68="\??\C:\SAMPLE\TANATOS.EXE")

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The minimal indivisible information unit used during the feature set formation is a terminal incident (event). The description of terminal incident (event) includes the following sets (Table 1): identifier (name) of the called function; values of operands transferred on the input of the function (input operands); values of results returned by the function. The process of feature extraction is carried out taking into account the last two sets. That fact favorably distinguishes the approach offered from that described in [9]. The process of feature extraction also considers (Table 2): amount of each function calls; amount of accesses to resources with identical importance rank; usage of specific resources (attempts of access to some specific keys of the system registry, to some system-related file folders, etc.); presence and quantity of specific chains of calls. Table 1. Intercepted Native API functions and theirs identifiers Id 037 116 183 274 179 248 139 149 151

a

Function name ZwCreateFile a ZwOpenFile a ZwReadFile a ZwWriteFile a ZwQueryVolumeInformationFile ZwSetVolumeInformationFile ZwQueryAttributesFile ZwQueryFullAttributesFile ZwQueryInformationFile

Id 224 145 047 048 041 063 065 119 247

Function name ZwSetInformationFile ZwQueryDirectoryFile ZwCreateProcess ZwCreateProcessEx ZwCreateKey a ZwDeleteKey a ZwDeleteValueKey ZwOpenKey ZwSetValueKey a

Input operands were taken into account

Table 2. Chosen features groups Id s_* r_[f|p|r]*

b_f*

Group name Samples Amounts of @ATTRIBUTE s_037 called functions NUMERIC (ZwCreateFile calls amount) Summary @ATTRIBUTE r_f1 amount of NUMERIC used resources (number of requests to critical file resources) Chains of @ATTRIBUTE b_f1 function calls NUMERIC (ZwOpenFile->ZwWriteFile) chains amount

Power of group 18 features for each intercepted function 7 features which specify amount of accesses to the system registry, files and process subsystem 5 features which specify the amount of typical malware/benign call chains


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As a part of the feature selection process, the search of call chains specific for the malware was provided by the Hamming distance formula. In further experiments the use of the Levenshtein distance formula is proposed. It is a measure of difference in two symbol sequences, defined as the minimum quantity of such operations as insert, remove, and replace, necessary to transform one line to another. 5.3 Learning and Test Results The results of our research (see Fig. 4 and Table 3), including the analysis of intermediate results, have clarified the issues of the efficiency evaluation of the proposed DM-based detection approach, and the directions of further development of the malware detection simulation tool so far used.

Fig. 4 Results of cross-validation for NB-based detection model Table 3 Summary results Classifier Decision Tree Naive Bayes Multilayer perceptron

Class Malicious Benign Malicious Benign Malicious Benign

TP rate 0.857 0.8 0.857 0.8 0.429 0.8

FP rate 0.2 0.143 0.2 0.143 0.2 0.571

These issues are as follows: the accessible malware sets have low quality not allowing the generating, without additional efforts, of the relevant training/test samples which can give accurate and well-founded results; the

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collected and analyzed malware call traces confirm the applicability of the simplified detection technologies focused on the malware classes chosen for the experiments, i.e., checking the selected keys of the system registry, the interprocess interaction mechanisms, and the file system events; using the generated feature and file test sets, the implemented classifiers showed the possibility of detection up to 80% of unknown malicious programs with mixed functionality classes “trojan/file worm/e-male worm” (for Win32 platform), where false positives indicator was about 15%; the statistical classifier group used satisfied the performance requirement better due to a good possibility of incremental re-learning.

6 Conclusion In the paper, the common DM-based approach to proactive malware detection was proposed. The specified approach allows the detection of the malware during its execution by classification methods using some specific features of the malware behavior. The DM-based behavior analysis is used as an interactive secretive process, trying to keep the requirements of detection accuracy, productivity, effective host resources consumption, the stability of detection functionality, and monitoring secrecy. The offered approach differs from the existing ones by cyclic interactive covert processing of behavioral information, and integrated use of different DM methods. A family of different data mining methods based on Bayes approach, decision trees, and neural networks was implemented and investigated. At the same time, the current results of experiments have shown the necessity to develop more careful procedures of extraction and selection of the behavioral features related to each malware class, as well as to expand the malware detection simulation tool used and improve it at the expense of applying the additional data.

Acknowledgments This research is supported by grant of the Russian Foundation of Basic Research (Project No. 07-01-00547), Program of fundamental research of the Department for Nanotechnologies and Informational Technologies of the Russian Academy of Sciences and partly funded by the EU as part of the RE-TRUST project.


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References 1. Aycock J (2006) Computer Viruses and Malware, Advances in Information Security, vol 22, Springer 2. Cios KJ, Pedrycz W, Swiniarski RW, and Kurgan LA (2007) Data Mining. A Knowledge Discovery Approach. Springer Science & Business Media 3. Pietrek M (2002) An In-Depth Look into the Win32 Portable Executable File Format. MSDN Magazine 4. Raffetseder T, Krugel C, and Kirda E (2007) Detecting System Emulators. LNCS, Springer, vol 4779 5. Schreiber SB (2002) Undocumented Windows 2000 secrets. A programming cookbook. Addison-Wesley 6. Schultz MG, Eskin E, Zadok E, and Stolfo SJ (2005) Data mining methods for detection of new malicious executables. Inform Comput Sci 172(1–2) 7. VX Heavens Site, http://vx.netlux.org/ 8. Wang J-H, Deng PS, Fan Y-S, Jaw L-J, and Liu Y-C (2003) Virus Detection using Data Mining Techniques. IEEE 37th Annual International Carnahan Confe-rence 9. Zhang B-Y, Yin J-P, Hao J-B, Zhang D-X, and Wang S-L (2006) Using support vector machine to detect unknown computer viruses. Inte J of Comput Intell Res 2(1)

Schemes for Data and User Authentication in Distributed GIS

Sergey Shtanko and Andrey Pankin St. Petersburg Institute for Informatics and Automation of the RAS, 39, 14 Liniya, St. Petersburg, 199178, Russia, [email protected]

Abstract. The paper specifies approaches to the realization of data and user authentication functions in distributed GIS, allowing for avoiding the existing protocols’ disadvantages, and thus accounting for the requirements for the geographical information system (GIS) safety. The authors propose the method of user’s private key storing at the server for the cases when the private key storing cannot be realized at the user’s end. A method binding the user’s identification process to the geographical location is proposed. Keywords: Distributed GIS; Electronic digital signature; Authentication

1 Introduction At deployment of distributed geographical information system (GIS) protection for different functions of geoinformation data including the cryptographic protection is often required. Depending on the GIS type and purpose, a demand for the data and user authentication as well as for the data encryption can arise. Therefore, the emerging problem springs from the requirement that a security system has to serve a large number of users while exchanging with them large amounts of heterogeneous information. In this regard, situations often occur when the users cannot store the key information at their ends, so the server would be the only repository available for the key information storing; the above assumes that the safety V.V. Popovich et al. (eds.), Information Fusion and Geographic Information Systems, Lecture Notes in Geoinformation and Cartography. DOI: 10.1007/978-3-642-00304-2_25, © Springer-Verlag Berlin Heidelberg 2009

359

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S. Shtanko, A. Pankin

level to be provided for the key storing should ensure the exclusive access of the key owner to the respectively assigned private key.

2 Existing Approaches to User Authentication Problems in Distributed GIS Consider the distributed GIS with a large number of work stations and one server. The users’ access to the system is not limited by some specified work station and could be executed from different work stations (terminals). There can be more servers, e.g., one for the ontology (may also be several of them), one for the cartographic information, etc. The ontology will be understood as the detailed formalization of a certain area by a conceptual scheme consisting of the data hierarchical structure, incorporating all relevant object classes, their relations, and rules (theorems, constraints) adopted for this area. At this point, one server for the objects’ ontology as used in GIS will be considered, e.g., the marine application both for displayable and nondisplayable objects (ships, ports, marches, missions, etc.). Such GIS can provide different data for different users. In this case, it is necessary to realize the function of users’ authentication and access sharing. For this purpose, procedures of the users’ identification and authentication at the system input [1, 2] will be used. To identify user A , let use some unique user A match information that is an identifier Id A , being the user’s code name in the simplest case (also called “login”). The server, as well as other users, identifies the user based on this unique information. The authentication procedure should be performed to prevent an intruder from the illegal use of the above specified identifier. This procedure can be performed using certain unique user inherent data. Such data can include biometric data of the user or some secret information known only to the user. In the simplest case such information can include password PASS A – some secret word; or set of characters known only to the user and stored in the database (DB) of the system users. To carry out the authentication procedure the user should enter the identifier and password. The identifiers’ validation system finds the user’s password and compares it with the entered one. If they coincide the user A will be authorized to access the system, otherwise the access will be denied. In order to prevent an intruder from access to the user’s password even when the intruder somehow manages to access the users’ DB or intercept the authentication information, the database stores instead of the user’s


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password its hash function hA = hash ( PASS A ) . Upon the user’s entering the password from the terminal, the password is hashed, and the hash function hA is transmitted to the server for authentication; thus, the password in the public form PASS A is not stored in the memory. In the simplest case a standard procedure of authentication applied in one form or another in existing systems has the following sequence (Fig. 1).

Fig. 1 User authentication

The described scheme is subject to intercepting attacks [1, 3]. The intruder can intercept the hashed password hA and use it even without knowing password PASS A . To prevent an attack of this type it is necessary to introduce some variable data into the hash function. At each authentication process it will allow the sending of different information to the server. For example, current time can be used as such information. Under this condition the hash function hA = hash ( PASS A + t ) will be different each time and useless for the interceptor, since the function argument will be varying. Nevertheless, this scheme will have the following disadvantage – the client

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terminal and the server have to be synchronized with accuracy equal to a single time count, which is really hard to do in practice. The other approach allowing for avoiding the strict requirements for synchronization is a usage of a random number r as an argument of the hash function instead of time marker t . In this case the algorithm can be as follows: the server upon the client A request generates a number r, transmits it to the client A , the client calculates hA = hash ( PASS A + r ) and transmits it to the server for the authentication. The latter two schemes have the following disadvantage: the client password PASS A has been stored at the server in the public form so that the server could perform the operation ( PASS A + r ) aimed at validating the received authentication data. The upgraded user authentication scheme can possibly eliminate the above disadvantage. Two latest schemes in various modifications are used in almost all protocols realizing the authentication procedures in computer networks. The authors have analyzed different symmetric and asymmetric protocols [1–3], including the particular one (Diffie–Hellman, Schnorr, Fiat–Shamir), and specified realizations, used in computer networks (IPSec, SSL/TLS), based on X.509 standard. None of the considered protocols simultaneously meets the entire set of requirements’ for the authentication procedures for the distributed GIS. 1. The information transmitted during the authentication procedure should be different at each session; thus preventing the intruder from using the intercepted data for the authentication. 2. The passwords in the public form should not be stored in the database; however, their storing in the hashed form should also prevent the intruder from their use even if the intruder managed to access the database. The system has to support storing the personal users’ keys on its server 3. The system should provide for the user’s binding to geographic co-ordinates. On the other hand, most of the considered protocols that are currently realized in the computer networks (based on X.509 standard) possess a large number of services not needed for the distributed GIS applications. So, many of them provide for using the servers’ certifiers that are excessive for the approach under consideration; to meet the above mentioned requirements and to eliminate the existing protocols’ disadvantages this upgraded authentication scheme has been developed.


363

3 Upgraded Scheme of User Authentication for Distributed GIS The upgraded scheme for user authentication is the development of the above discussed schemes; it allows for eliminating the disadvantages of the authentication process. While developing a client, his Id A and hash ( PASS A + Id A ) value are put into the database. The server S possesses a pair of keys for asymmetric cryptosystem: the private key S and public key kpublic . To enter the system (Fig. 2), the client A calculates hash ( PASS A + Id A ) , generates a random number r , requests the public key from the server S and encrypts it ticket = encrypt ( ( hash ( PASS A + Id A ) + r ) ; kpublic ) (hereinafter encrypt (m; k ) – process of message m encryption with k key, decrypt (c; k ) – process of cryptogram c decryption with k key; s = sign(m; k ) – process of signature s generation for message m with k key; m = signver ( s; k ) – process of verification of signature s under message m with k key). The server after receipt of ticket’ decrypts it by its private S S key kprivate : hash ′ ( PASS A + Id A ) + r ′ = decrypt ( ticket, kprivate ) . Then the server validates hash ( PASS A + Id A ) = hash ′ ( PASS A + Id A ) and in the case of their matching grants the client A an access. The system described allows for storing the passwords in DB in the hashed form, thus protecting them against the intruder in case of the hackers’ attack against DB and theft, and transmitting the authenticating data in the form of encrypted tickets, which are changed at each authentication session, thus preventing the ticket intercepting intruder from using it. ?

4 Upgraded Scheme of User Authentication for Distributed GIS with Possibility of Private Keys for Authentication Data Storage at the Server To implement in GIS the functions of data authentication and encryption it is also reasonable to apply asymmetric cryptosystems. However, for the direct data encryption it is expedient to use symmetric cryptosystems as they are faster; therefore, the asymmetric systems are used for generating the keys for symmetric systems and data authentication (realizing the electronic digital signature (EDS)). To develop such a system it is necessary that every system’s user has a pair of keys – the private and public ones.

364


S k public

S k private

S k public

S k public

Fig. 2 Upgraded user authentication

One of the approaches includes the use of different electronic media (similar to bank cards) where the user’s private key would be stored in encrypted form. During the authentication process the user enters the password, which serves as the key for the private key decryption for its further use in the processes of data authentication and encryption. In some cases the application of the scheme with electronic media cannot be realized. Then the private key cannot be stored at the user’s PC. The only solution in this case is storing the users’ keys pairs in DB at the server. The following scheme for the users’ authentication and use of asymmetric cryptosystems is proposed for the case when the key information can be stored only at the server. In this case the record of the user А will include identifier (login) Id A , hashed password in the form of hash ( PASS A + Id A ) , А A public key kpublic , encrypted private key kprivate . The private key is encrypted


365

by the symmetric algorithm, whose key is generated based on the password and transmitted to the user on request in the encrypted form. The user using his private password can decrypt the private key to perform different cryptographic operations, e.g., for EDS generation. 4.1 Example of Authentication Scheme Realization and User’s Keys Storage in Distributed GIS Creation of the user’s record: 1. User A generates identifier (login) Id A and password PASS A . A 2. User A generates a secret key ksecret for symmetric system for the A purpose of private key kprivate encryption. 3. User A calculates hA = hash ( PASS A + Id A ) . A = f ( hA ) . In the simplest case 4. User A calculates an auxiliary key k temp hash function hA or its part can be used as such key. 5. User A calculates cryptogram

((

)

)

A A CRYPTPASS A = encrypt hA ksecret ; ktemp through the symmetric algorithm. 6. User A generates a pair of keys for symmetric algorithm: public key А A kpublic and private key kprivate . 7. User A encrypts the private key by symmetric algorithm with secret A A A A key ksecret : ekprivate = encrypt ( kprivate ; ksecret ). А A , ekprivate to the server. 8. User A transmits Id A , CRYPTPASS A , kpublic 9. The server stores receives information about a new user in DB in the form (Table 1).

Table 1. Example of the user’s record storage in DB

Index …

Password … CRYPTPASS A

Privatekey … A ekprivate

Publickey …

iA

Login … Id A

…

…

…

…

…

А kpublic

The user authentication scheme for this case will acquire a form represented in Fig. 3. Actions performed by the client do not differ from actions described in the previous scheme (Fig. 2). Nevertheless, CRYPTPASS A , and not hA , will be stored on the servers in the users’ DB. To receive hA the server has to perform an operation inverse to the operation performed

366


S k public

S k private

S k public

A k temp

А ksec ret

k

S public

A k temp

Fig. 3 Upgraded user authentication for the case when the private key is stored in DB at the server A A when creating a user hA ksecret = decrypt ( CRYPTPASS A , k temp ) . Thus, the key A k temp is calculated from hA transmitted by the client in the similar way as

A when creating the user: ktemp = f ( hA′ ) . The same authentication procedure is also performed at the user’s private key request for different purposes (e.g., for EDS realization). Upon


367

A the successful authentication of values, CRYPTPASS A and ekprivate are transmitted to the user. To receive his own private key and to generate a signature the user will perform the following operations: Generation of EDS: 1. User A calculates hA′ = hash ( PASS A + Id A ) . A 2. User A calculates auxiliary key k temp = f ( hA′ ) . A A = decrypt ( CRYPTPASS A , k temp 3. User A calculates hA ksecret ). A A A 4. User A calculates kprivate = decrypt ( ekprivate ; ksecret ).

A 5. User A signs the message m : s A = sign ( m; kprivate ).

Now, the signature of user A can be validated both by the server and A any other user using the public key of user A kpublic . EDS validation: 1. The server (User B ) receives the signed message m s . A 2. The server (User B ) calculates m′ = signver ( s; kpublic ). ? 3. The server (User B ) validates signature m′ = m (in practice

?

h′(m) = h (m). Thus, the above system allows construction of sufficiently protected, and at the same time undemanding for resources, systems of the user authentication.

5 Binding the Distributed GIS User to Geographic Coordinates In some cases in distributed GIS a function of the user fixation to a certain position (region) can be required, i.e., in other words the user is entitled to be connected to the system only in a certain geographic zone. In this case geographic coordinates received from the navigation sensor become auxiliary arguments of hash function. In this case the table of the user account in DB will have the form (Table 2). Table 2 Example of the user’s record with coordinates fixation in DB

Index …

Login …

Password …

Privatekey …

Publickey …

Lat …

iA

Id A

CRYPTPASS A

A ek private

А k public

lat A

…

…

…

…

…

…

Lon …

lon A …

368


While creating the user’s record the coordinates of the user lat A , lon A are added to the hash function arguments; they are received from the navigation system and approximated to a certain value, so that the discreteness of approximated coordinates does not exceed the error of position determining: hA = hash ( PASS A + Id A + lat A + lon A ) . Further, in each process of authentication at entering the identifier and password by the user into the terminal, the coordinates of this terminal are added automatically to them. Thus, if the user tries to enter the system from another position, and value hA received from the user and value h′A received from DB do not match, the user will be denied access.

6 Conclusion The proposed approaches and schemes allow the effective protection of information circulating in distributed GIS with consideration of the imposed constraints. In particular, the scheme is suggested for using the asymmetric cryptosystems for the case when users cannot store key information and it must be stored at the server. Also, the approach is proposed allowing for binding the user to a certain geographic position. The paper provides schemes not binding to specific cryptographic algorithms. Any algorithms depending on the specified requirements and legal issues can be used in the described schemes as symmetric and asymmetric algorithms. Thus, this scheme has been realized in GIS developed using J2EE platform based upon the embedded JCA and JASS tools with the use of symmetric AES and asymmetric RSA algorithms. It is worth noting that the considered standard X.509 is not associated with certain algorithms; however, it is suggested to use Diffie-Hellman and RSA algorithms as the asymmetric ones. Using algorithms based on elliptic curves seems the most preferable due to their better robustness and encrypting performance [4, 5]. For instance, the elliptic curves algorithms at the key size (capacity of the module p binary representation) of 220–260 bit provide for higher robustness and encrypting performance than RSA algorithms at the key size of 2,048 bit. The study of the elliptic curves-based asymmetric algorithms to be realized in Java and intended for implementing in the above schemes also lies within the scope of the authors’ research interests.


369

References 1. Zima VM, Moldovyan AA, and Moldovyan NA (2003) The Global Network Technologies Safety (in Russian). BHV–Petersburg, St. Petersburg, Russia 2. Stallings W (2001) Cryptography and Network Security: Principles and Practice (in Russian). Williams Publishing House, Moscow, Russia 3. Ivanov MA (2001) Cryptographic Methods of Information Protection in Computer Systems and Networks (in Russian). KUDITS–OBRAS, Moscow, Russia 4. Moldovyan NA and Moldovyan AA (2005) Introduction in Cryptosystems with Public Key (in Russian). BHV–Petersburg, St. Petersburg, Russia 5. Rostovtsev AG and Makhovenko EB (2001) Introduction in Cryptography with Public Key (in Russian). Mir i Semya, St. Petersburg, Russia 6. Schneier B (1996) Applied Cryptography: Protocols, Algorithms, and Source Code in C. Willey, New York, USA 7. Barker E, Barker W, Burr W, Polk W, and Smid M (2006) NIST Special Publication 800-57, Recommendation for Key Management. http://csrc.nist.gov/ publications/nistpubs/800-57/SP800-57-Part1.pdf 8. Frankel S, Hoffman P, Orebaugh A, and Park R (2008) NIST Special Publication 800-113, Guide to SSL VPNs, http://csrc.nist.gov/publications/nistpubs/ 800-113/SP800-113.pdf 9. Hankerson D, Menezes A, and Vanstone SA (2004) Guide to Elliptic Curve Cryptography. Springer, Berlin Heidelberg New York 10. Anoop MS (2007) Elliptic Curve Cryptography – An Implementation Tutorial. Tata Elxsi, India 11. The Case for Elliptic Curve Cryptography. National Security Agency. http:// www.nsa.gov/ia/industry/crypto_elliptic_curve.cfm 12. JavaTM Cryptography Architecture. API Specification & Reference. http:// java.sun.com/j2se/1.5.0/docs/guide/security/CryptoSpec.html

Author Index

Andrienko, G. 3 Andrienko, N. 3 Angelaccio, M. 137

Levachkine, S. 77, 149, 165, 177 Levashova, T. 113 Lobo, V. 19 Lopez-Pellicer, F. J. 193 Lubniewski, Z. 251

Bikonis, K. 251 Bosse, E. 209 Bruniecki, K. 277

Makshanov, A. 265, 289 Martinez, M. 165 Mata, F. 77 Moldovyan, A. 317 Moldovyan, N. 317, 331 Moldovyanu, P. 331 Moreno, M. 149, 177 Moszynski, M. 277

Chen, S. 95 Chybicki, A. 263 Claramunt, C. 95 Corcho, O. 193 D’Ambrogio, A. 137 Dabrowski, J. 277 Dernova, E. 331 Desnitsky, V. 301 Duclos–Hindie, N. 209 Ermolaev, V.

265, 289

Florea, M. C.

209

Nogueras-Iso, J. Osipov, V.

241

Pankin, A. 63, 359 Popovich, V. 39, 227

Galjano, P. 227 Guzman, G. 149, 177 Ivakin, Y.

193

Quintero, R.

149, 177

Ramos, J. A. Ray, C. 95

193

241 Shilov, N. 113 Shtanko, S. 359 Smirnov, A. 113 Stepnowski, A. 251, 277

Kaminski, L. 277 Kashevnik, A. 113 Komashinskiy, D. 343 Kostina, A. 331 Kotenko, I. 301, 343 Kozlovskiy, S. 265 Krek, A. 137 Kulawiak, M. 251, 277 Kuzenny, V. 63

Tan, J. 95 Tarakanov, A. 125 Torres, M. 149, 177 Valin, P. 209 Vilches-Blazquez, L. M. 371

193

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