Recent Trends in Information Reuse and Integration

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¨ Tansel Ozyer Keivan Kianmehr Mehmet Tan Editors

Recent Trends in Information Reuse and Integration

Editors ¨ Tansel Ozyer Department of Computer Engineering Tobb University S¨og˘ u¨ t¨oz¨u Caddesi No. 43, S¨og˘ u¨ t¨oz¨u Ankara, Turkey [email protected]

Mehmet Tan Department of Computer Engineering Tobb University S¨og˘ u¨ t¨oz¨u Caddesi No. 43, S¨og˘ u¨ t¨oz¨u Ankara, Turkey [email protected]

Keivan Kianmehr Department of Electrical and Computer Engineering University of Western Ontario Building 373 London, ON, N6A 5B9, Canada [email protected]

This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically those of translation, reprinting, re-use of illustrations, broadcasting, reproduction by photocopying machines or similar means, and storage in data banks. Product Liability: The publisher can give no guarantee for all the information contained in this book. This does also refer to information about drug dosage and application thereof. In every individual case the respective user must check its accuracy by consulting other pharmaceutical literature. The use of 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. c 2012 Springer-Verlag/Wien  SpringerWienNewYork is part of Springer Science + Business Media springer.at Typesetting: SPi Publisher Services Printed on acid-free paper and chlorine-free bleached paper SPIN: 80032388

With 134 (partly coloured) Figures Library of Congress Control Number: 2011937549 ISBN 978-3-7091-0737-9 e-ISBN 978-3-7091-0738-6 DOI 10.1007/978-3-7091-0738-6 SpringerWienNewYork

Foreword

We are delighted to see this edited book as the result of our intensive work over the past year. We succeeded in attracting high quality submissions of which we could only include 19 papers in this edited book. The present text aims at helping the reader whether researcher or practitioner to grasp the basic concept of reusability which is very essential in this rapidly growing information era. The authors emphasize the need for reusability and how it could be adapted in the right way. Actually reusability leads to satisfy a multiobjective optimization process, that is, to minimize the time, cost and effort spent to develop new products, technologies, information repositories, etc. Instead of always starting from scratch and reinventing the wheel in a process that consumes and wastes time, effort, and resources, practitioners and developers should always look into the possibility of reusing some of the existing entities to produce new ones. In other words, reuse and integration are essential concepts that must be enforced to avoid duplicating the effort. This problem is investigated from different perspectives. In organizations, high volumes of data from different sources form a big threat for filtering out the information for effective decision making. To address all these vital and serious concerns, this book covers the most recent advances in information reuse and integration. It contains high quality research papers written by experts in the field. Some of them are extended versions of the best papers which were presented at IEEE International Conference on Information Reuse and Integration, which was held in Las Vegas in August 2010. Chapter 1 by Udai Shanker, B. Vidya Reddi, and Anupam Shukla studies a real time commit protocol to improve the performance based on two approaches. They are: 1. Some of the locked data items which are unused after completion of processing of transaction can be unlocked immediately after end of processing phase to reduce data contention. 2. A lending transaction can lend its dirty data to more than one cohorts by creating only a single shadow in case of write–read conflicts to reduce the data inaccessibility. Its performance has been compared with existing protocols.

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Foreword

Chapter 2 by Ladjel Bellatreche estimates the complexity of the problem of selecting dimension table(s) to partition a fact table. It also proposes strategies to perform their selection that take into account the main characteristics of queries such as access frequencies, size of tables, size of intermediate results of joins, etc. Experimental studies using a mathematical cost model and the obtained results have been executed on Oracle11G DBMS for validation, and a tool has been implemented to assist data warehouse administrators in their horizontal partitioning selection tasks. Chapter 3 by Shabnam Pourdehi, Dena Karimipour, Navid Noroozi, and Faridoon Shabani addresses an adaptive fuzzy controller for a large class of nonlinear systems in the presence of uncertainties, input nonlinearities, and unknown timedelay. Based on the combination of the sliding mode control (SMC) with fuzzy adaptive control, it presents a design algorithm to synthesize a robust fuzzy sliding mode controller. Later, an adaptive fuzzy observer-based SMC scheme is proposed for stabilization. The unknown nonlinear functions have been approximated with fuzzy logic systems in both proposed control schemes and its asymptotical stability according to the corresponding closed-loop system is shown with Lyapunov– Krasovskii approach. The synchronization of two nonidentical time-delayed chaotic systems is investigated as an application of control schemes with simulated examples to show the effectiveness of the proposed techniques. Chapter 4 by Ladjel Bellatreche, Guy Pierra, and Eric Sardet presents a technique for integrating automatically ontology-based data sources in a materialized architecture and a framework dealing with the asynchronous versioning problem. Existing solutions proposed in traditional databases is adapted to manage instance and schema changes. The problem of managing ontology changes is overcome by introducing a distinction between ontology evolution and ontology revolution. Finally, it proposes floating version model for ontology evolution. It fully automates the whole steps of building an ontology-based integration systems (OBIS). It is validated by a prototype using ECCO environment and the EXPRESS language. Chapter 5 by Iyad Suleiman, Shang Gao, Maha Arslan, Tamer Salman, Faruk Polat, Reda Alhajj, and Mick Ridley suggests a a computerized assessment tool that can learn the user’s skills and adjust the assessment tests for assessment of school readiness. The user plays various sessions from various games, while the Genetic Algorithm (GA) selects the upcoming session or group of sessions to be chosen for the user according to his/her skills and status. It describes the modified GA and the learning procedure that is integrated with a penalizing system into the GA and a fitness heuristic for best choice selection. Two methods for learning are proposed, a memory system and a no memory system. Furthermore, it includes several methods for the improvement of the speed of learning. In addition, learning mechanisms that are based on the social network paradigm to address further usage of assessment automation is used. Chapter 6 by Gr´egory Claude, Ga¨el Durand, Marc Boyer, and Florence S`edes defends the idea that a defect carries information by the simple fact that it exists, the characteristics of the detected incident (the problem) and of the applied protocol to resolve it (the solution). The various actors who took part in its description and

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its resolution collect this information. This knowledge is essential to achieve for assistance in corrective actions for future defects and prevention of their emergence. Taking the advantage of this knowledge by working out a model of defect makes it possible to define a set of grouping criteria of defects that were solved in the past. These groups are the cornerstone of the corrective and preventive processes for new defects. Chapter 7 by Abdullah M. Elsheikh, Tamer N. Jarada, Taher Naser, Kelvin Chung, Armen Shimoon, Faruk Polat, Panagiotis Karampelas, Jon Rokne, Mick Ridley, and Reda Alhajj presents a comprehensive approach for the mapping between the object database language (ODL) and XML. It includes both structure specification and database content. It concentrates on deriving a separate set of transformation rules for each way mapping. For the first mapping (from ODL to XML). Chapter 8 by Taghi M. Khoshgoftaar, Kehan Gao, and Jason Van Hulse proposes a novel approach to feature selection for imbalanced data in the context of software quality engineering. This process follows a repetitive process of data sampling followed by feature ranking and finally aggregating the results generated during the repetitive process. It is compared against filter-based feature ranking technique alone on the original data, and data sampling and feature ranking techniques with two different scenarios. Chapter 9 by Zifang Huang and Mei-Ling Shyu presents a k-nearest neighbors (k-NN)-based least squares support vector machine (LS-SVM) model with multivalue integration to tackle the long-term time series prediction problem. A new distance function, which incorporates the Euclidean distance and the dissimilarity of the trend of a time series, is deployed. Chapter 10 by Carlos M. Cornejo, Iv´an Ruiz-Rube, and Juan Manuel Dodero discusses their approach to provide a complete set of services and applications to integrate diverse web-based contents of the cultural domain. It is purposed to extend over the web the knowledge base of cultural institutions, build user communities around it, and enable its exploitation in several environments. Chapter 11 by Abdelghani Bakhtouchi, Ladjel Bellatreche, Chedlia Chakroun, and Yamine A¨ıt-Ameur proposes an ontology-based integration system with mediator architecture. It exploits the presence of ontology referenced by selected sources to explicit their semantic. Instead of annotating different candidate keys of each ontology class, its set of functional dependencies are defined on its properties. Chapter 12 by Mark McKenney gives an efficient algorithm for the map construction algorithm (MCP). The algorithm is implemented and experiments show that it is significantly faster than the naive approach, but is prone to large memory usage when run over large data sets. An external memory version of the algorithm is also presented that is efficient for very large data sets, and requires significantly less memory than the original algorithm. Chapter 13 by Jairo Pava, Fausto Fleites, Shu-Ching Chen, and Keqi Zhang proposes a system that integrates storm surge projection, meteorological, topographical, and road data to simulate storm surge conditions. The motivation behind the

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system is to serve local governments seeking to overcome difficulties in persuading residents to adhere to evacuation notices. Chapter 14 by Richa Tiwari, Chengcui Zhang, Thamar Solorio, and WeiBang Chen describes a framework to extract information about co-expression relationships among genes from published literature using a supervised machine learning approach. Later it rank those papers to provide users with a complete specialized information retrieval system with Dynamic Conditional Random Fields (DCRFs) for training the model. They show its superiority against Bayes Net, SVM, and Nave Bayes. Chapter 15 by Brandeis Marshall identifies similar artists, which serves as a precursor to how music recommendation can handle the more complex issues of multiple genre artists and artist collaborations. It considers the individual most similar artist ranking from three public-use Web APIs (Idiomag, Last.fm, and Echo Nest) as different perspectives of artist similarity. Then it aggregates these three rankings using five rank fusion algorithms. Chapter 16 by Ken Q. Pu and Russell Cheung presents a query facility called tag grid to support fuzzy search and queries of both tags and data items alike. By combining methods of Online Analytic Processing from multidimensional databases and collaborative filtering from information retrieval, tag grid enables users to search for interesting data items by navigating and discovering interesting tags. Chapter 17 by Stefan Silcher, Jorge Minguez, and Bernhard Mitschang introduces an SOA-based solution to the integration of all Product Lifecycle Management (PLM) phases. It uses an Enterprise Service Bus (ESB) as service-based integration and communication infrastructure. Three exemplary scenarios are used to illustrate the benefits of using an ESB as compared to alternative PLM infrastructures. Furthermore, it describes a service hierarchy that extends PLM functionality with value-added services by mapping business processes to data integration services. Chapter 18 by Awny Alnusair and Tian Zhao describes an ontology-based approach for identifying and retrieving relevant software components in large reuse libraries. It exploits the use of domain-specific ontologies to enrich a knowledge base initially populated with ontological descriptions of API components. Chapter 19 by Du Zhang proposes an algorithm for detecting several types of firewall rule inconsistency. It also defines a special type of inconsistency called setuid inconsistency and highlights various other types of inconsistencies in the aforementioned areas. Finally, it concludes that inconsistency is a very important phenomenon, and its utilities can never be underestimated in information security and digital forensics. Last but not the least, we would like to mention the hard workers behind the scene who have significant unseen contributions to the successful task that produced this valuable source of knowledge. We would like to thank the authors who submitted papers and the reviewers who produced detailed constructive reports which improved the quality of the papers. Various people from Springer deserve

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large credit for their help and support in all the issues related to publishing this book. In particular, we would like to thank Stephen Soehnlen for his dedications, seriousness, and generous support in terms of time and effort; he answered our emails on time despite his busy schedule, even when he was traveling. ¨ Tansel Ozyer, Keivan Kianmehr, Mehmet Tan

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Contents

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PERDURABLE: A Real Time Commit Protocol . . .. . . . . . . . . . . . . . . . . . . . Udai Shanker, B. Vidyareddi, and Anupam Shukla

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Dimension Table Selection Strategies to Referential Partition a Fact Table of Relational Data Warehouses . . . . . . . . . . . . . . . . . Ladjel Bellatreche

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Observer-Based Adaptive Fuzzy Control of Nonlinear Time-Delay Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . Shabnam Pourdehi, Dena Karimipour, Navid Noroozi, and Faridoon Shabani

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Evolution Management of Data Integration Systems by the Means of Ontological Continuity Principle . .. . . . . . . . . . . . . . . . . . . . Ladjel Bellatreche, Guy Pierra, and Eric Sardet

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Automated Learning for Assessment of School Readiness: Effectiveness of Reusing Information from Child’s Social Network . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . Iyad Suleiman, Shang Gao, Maha Arslan, Tamer Salman, Faruk Polat, Reda Alhajj, and Mick Ridley

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From Simple Management of Defects to Knowledge Discovery to Optimize Maintenance .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 115 Gr´egory Claude, Marc Boyer, Ga¨el Durand, and Florence S`edes

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Rules for Effective Mapping Between Two Data Environments: Object Database Language and XML . . . . . . . . . . . 143 Tamer N. Jarada, Abdullah M. Elsheikh, Taher Naser, Kelvin Chung, Armen Shimoon, Panagiotis Karampelas, Jon Rokne, Mick Ridley, and Reda Alhajj

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Feature Selection for Highly Imbalanced Software Measurement Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 167 Taghi M. Khoshgoftaar, Kehan Gao, and Jason Van Hulse

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Long-Term Time Series Prediction Using k-NN Based LS-SVM Framework with Multi-Value Integration .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 191 Zifang Huang and Mei-Ling Shyu

10 Semantic Management of Digital Contents for the Cultural Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 211 Carlos M. Cornejo, Iv´an Ruiz-Rube, and Juan Manuel Dodero 11 Mediated Data Integration Systems Using Functional Dependencies Embedded in Ontologies . . . . . . . . . . . . . 227 Abdelghani Bakhtouchi, Chedlia Chakroun, Ladjel Bellatreche, and Yamine A¨ıt-Ameur 12 Algorithms for Spatial Data Integration .. . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 257 Mark McKenney 13 Integrating Geographic and Meteorological Data for Storm Surge Planning.. . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 273 Jairo Pava, Fausto Fleites, Shu-Ching Chen, and Keqi Zhang 14 A Supervised Machine Learning Approach of Extracting and Ranking Published Papers Describing Coexpression Relationships among Genes . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 293 Richa Tiwari, Chengcui Zhang, Thamar Solorio, and Wei-Bang Chen 15 Music Artist Similarity Aggregation . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 315 Brandeis Marshall 16 Tag Grid: Supporting Multidimensional Queries of Tagged Datasets .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 331 Ken Q Pu and Russell Cheung 17 A Novel Approach to Product Lifecycle Management based on Service Hierarchies .. . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 343 Stefan Silcher, Jorge Minguez, and Bernhard Mitschang 18 Retrieving Reusable Software Components Using Enhanced Representation of Domain Knowledge . .. . . . . . . . . . . . . . . . . . . . 363 Awny Alnusair and Tian Zhao 19 The Utility of Inconsistency in Information Security and Digital Forensics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 381 Du Zhang

Contributors

Yamine A¨ıt-Ameur LISI/ENSMA – Poitiers University Futuroscope, France, [email protected] Reda Alhajj Computer Science Department, University of Calgary, Calgary, AB, Canada Department of Computer Science, Global University, Beirut, Lebanon Department of Information Technology, Hellenic American University, Manchester, NH, USA, [email protected] Awny Alnusair Department of Science, Math & Informatics, Indiana University Kokomo, 2300 S. Kokomo, IN 46904, Washington, [email protected] Maha Arslan Haifa University, Haifa, Israel Abdelghani Bakhtouchi National High School for Computer Science (ESI), Algiers, Algeria, a [email protected] Ladjel Bellatreche LISI/ENSMA – Poitiers University, 1, avenue Cl´ement Ader, 86960 Futuroscope, France, [email protected] Marc Boyer Universit´e de Toulouse, Universit´e Paul Sabatier, Inserm UMR 825 Place du Docteur Baylac, Pavillon Riser 31059 Toulouse cedex 9, France, Marc. [email protected] Chedlia Chakroun LISI/ENSMA – Poitiers University Futuroscope, France, [email protected] Shu-Ching Chen Distributed Multimedia Information Systems Laboratory, Florida International University, Miami, FL 33199, USA, [email protected] Wei-Bang Chen Department of Computer and Information Sciences, The University of Alabama at Birmingham, 115A Campbell Hall, 1300 University Boulevard, Birmingham, Alabama 35294, [email protected]

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Contributors

Russell Cheung University of Ontario Institute of Technology, 2000 Simcoe Street N, Oshawa, ON, Canada, [email protected] Kelvin Chung Computer Science Department, University of Calgary, Calgary, AB, Canada Gr´egory Claude Universit´e de Toulouse, Universit´e Paul Sabatier, IRIT UMR 5505 118, route de Narbonne 31062, Toulouse cedex 9, France, Gregory. [email protected] Carlos M. Cornejo Computer Languages and Systems Department, University of Cadiz, CnChile. 1, 11002 Cadiz, Spain, [email protected] Juan Manuel Dodero Computer Languages and Systems Department, University of Cadiz, CnChile. 1, 11002 Cadiz, Spain, [email protected] Ga¨el Durand Intercim LLC, 1915 Plaza Drive, Eagan, MN 55122, USA Intercim, 32, rue des Jeˆuneurs 75002, Paris, France, [email protected] Abdullah M. Elsheikh Computer Science Department, University of Calgary, Calgary, AB, Canada Fausto Fleites Distributed Multimedia Information Systems Laboratory, Florida International University, Miami, FL 33199, USA, [email protected] Kehan Gao Eastern Connecticut State University, Willimantic, CT 06226, USA, [email protected] Shang Gao Computer Science Department, University of Calgary, Calgary, Alberta, Canada Zifang Huang Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL 33124, USA, [email protected] Tamer N. Jarada Computer Science Department, University of Calgary, Calgary, AB, Canada Panagiotis Karampelas Department of Information Technology, Hellenic American University, Manchester, NH, USA Dena Karimipour School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran Taghi M. Khoshgoftaar Florida Atlantic University, Boca Raton, FL 33431, USA, [email protected] Brandeis Marshall Computer and Information Technology, Purdue University, West Lafayette, IN 47907, USA, [email protected] Mark McKenney Department of Computer Science, Texas State University, 601 University Drive, San Marcos, TX 78666-4684, USA, [email protected]

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Jorge Minguez Graduate School for advanced Manufacturing Engineering, GSaME, Institute for Parallel and Distributed Systems, IPVS, University of Stuttgart, Universitaetsstrasse 38, 70569 Stuttgart, Germany, jorge.minguez@ipvs. uni-stuttgart.de Bernhard Mitschang Graduate School for advanced Manufacturing Engineering, GSaME, Institute for Parallel and Distributed Systems, IPVS, University of Stuttgart, Universitaetsstrasse 38, 70569 Stuttgart, Germany, bernhard. [email protected] Taher Naser Department of Computing, School of Computing Informatics and Media, University of Bradford, Bradford, UK Navid Noroozi School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran Jairo Pava Distributed Multimedia Information Systems Laboratory, Florida International University, Miami, FL 33199, USA, [email protected] Guy Pierra LISI/ENSMA – Poitiers University, 1, avenue Cl´ement Ader, 86960 Futuroscope, France, [email protected] Faruk Polat Dept of Computer Engineering, Middle East Technical University, Ankara, Turkey Shabnam Pourdehi School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran, Shabnamp [email protected] Ken Q Pu University of Ontario Institute of Technology, 2000 Simcoe Street N, Oshawa, ON, Canada, [email protected]@mycampus.uoit.ca Mick Ridley Department of Computing, School of Computing Informatics and Media, University of Bradford, Bradford, UK Jon Rokne Computer Science Department, University of Calgary, Calgary, AB, Canada Iv´an Ruiz-Rube Computer Languages and Systems Department, University of Cadiz, CnChile. 1, 11002 Cadiz, Spain, [email protected] Tamer Salman Dept of Computer Science, Technion, Haifa, Israel Eric Sardet CRITT Informatique CRCFAO, 86960 Futuroscope, France, [email protected] Florence S`edes Universit´e de Toulouse, Universit´e Paul Sabatier, IRIT UMR 5505 118, route de Narbonne 31062, Toulouse cedex 9, France, [email protected] Faridoon Shabani School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran, [email protected] Udai Shanker Dept. of CSE, M. M. M. Engg. College, Gorakhpur-273010, India, [email protected]

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Armen Shimoon Computer Science Department, University of Calgary, Calgary, AB, Canada Anupam Shukla ICT Stream, ABV IIITM Gwalior, India, dranupamshukla@ gmail.com Mei-Ling Shyu Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL 33124, USA, [email protected] Stefan Silcher Graduate School for advanced Manufacturing Engineering, GSaME, Institute for Parallel and Distributed Systems, IPVS, University of Stuttgart, Universitaetsstrasse 38, 70569 Stuttgart, Germany, stefan.silcher@ipvs. uni-stuttgart.de Thamar Solorio Department of Computer and Information Sciences, The University of Alabama at Birmingham, 115A Campbell Hall, 1300 University Boulevard, Birmingham, Alabama 35294, [email protected] Iyad Suleiman Department of Computing, University of Bradford, Bradford, UK Richa Tiwari Department of Computer and Information Sciences, The University of Alabama at Birmingham, 115A Campbell Hall, 1300 University Boulevard, Birmingham, Alabama 35294, [email protected] Jason Van Hulse Florida Atlantic University, Boca Raton, FL 33431, USA, [email protected] B. Vidyareddi ICT Stream, ABV IIITM Gwalior, India, vidyareddi [email protected] Chengcui Zhang Department of Computer and Information Sciences, The University of Alabama at Birmingham, 115A Campbell Hall, 1300 University Boulevard, Birmingham, Alabama 35294, [email protected] Du Zhang Department of Computer Science, California State University, Sacramento, CA 95819-6021, USA, [email protected] Keqi Zhang International Hurricane Research Center, Florida International University, Miami, FL 33199, USA, [email protected] Tian Zhao Department of Computer Science, University of WisconsinMilwaukee, 2200 E. Kenwood Blvd, P.O. Box 413, Milwaukee, WI 53211, USA, [email protected]

Chapter 1

PERDURABLE: A Real Time Commit Protocol Udai Shanker, B. Vidyareddi, and Anupam Shukla

1.1 Introduction Real Time Systems (RTS) are those systems, for which, correctness depends not only on the logical properties of the produced results but also on the temporal properties of these results [27]. Typically, RTS are associated with critical applications in which human lives or expensive machineries may be at stake. Examples include telecommunication systems, trading systems, online gaming, chemical plant control, multi point fuel injection system (MPFI), video conferencing, missile guidance system, sensor networks etc. Hence, in such systems, an action performed too late (or too early) or a computation which uses temporally invalid data may be useless and sometimes harmful even if such an action or computation is functionally correct. As RTS continue to evolve, their applications become more and more complex, and often require timely access and predictable processing of massive amounts of data [21]. The database systems especially designed for efficient processing of these types of real time data are referred as distributed real time database system (DRTDBS). Here, data must be extremely reliable and available as any unavailability or extra delay could result in heavy loss. Business transactions being used in these applications in the absence of real time could lead to financial devastations and in worst case cause injuries or deaths [20]. The applications listed here using DRTDBS require distributed transaction executed at more than one site. The transaction can be of read, blind write or update type having timing constraints. A commit protocol ensures that either all the effects of the transaction persist or none of them persist despite the failure of

U. Shanker () Dept. of CSE, M.M.M. Engg. College, Gorakhpur-273010, India e-mail: [email protected] B. Vidyareddi  A. Shukla ICT Stream, ABV IIITM Gwalior, India e-mail: vidyareddi [email protected]; [email protected] ¨ T. Ozyer et al. (eds.), Recent Trends in Information Reuse and Integration, DOI 10.1007/978-3-7091-0738-6 1, © Springer-Verlag/Wien 2012

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site or communication link and loss of messages [5]. Hence, the commit processing should add as little overhead as possible to transaction processing. Therefore, design of a better commit protocol is very much important for DRTDBS.

1.2 Related Work A distributed system is one where data are located on several computers that are linked together by a heterogeneous network. The advantages of such system are increased availability of resources, increased reliability and increased execution speed in less time. The coordination of activities among these computers is a complex task and deadlines make them more complex [6]. If a transaction runs across more than one site, it may commit at one site and may fail at another site. Hence, uniform commitment is needed to ensure that all participating sites agree on a final outcome. The two phase commit protocol (2PC) referred to as the Presumed Nothing 2PC protocol (PrN) is the most widely used commit protocol for maintaining atomicity in distributed real time systems [25]. It ensures that sufficient information is force-written on the stable storage to reach a consistent global decision about the transaction. In case of faults, however, it is not possible to provide such guarantees and an exception state is allowed which indicates the violation of the deadline. The blocking nature of 2PC can make the executing-committing conflict a serious problem to the system performance. A number of 2PC variants [13] have been proposed and can be classified into following four groups [2]. • • • •

Presumed Abort/Presumed Commit Protocols One Phase Commit Protocols [12] Group Commit Protocols Pre Commit/Optimistic Commit Protocols

Presumed commit (PC) and presumed abort (PA) [14] are based on 2PC. PA, as originally designed and implemented in R*, does not support coordinator migration. [26] have proposed a protocol that allows individual site to unilaterally commit. If later on it is found that the decision is not consistent globally then compensation transactions are executed to rectify errors. The problem with this approach is that many actions are irreversible in nature. Gupta et al. proposed optimistic commit protocol and its variant in [8]. Enhancement has been made in PROMPT [9] commit protocol, which allows executing transactions to borrow data in a controlled manner only from the healthy transactions in their commit phase. However, it does not consider the type of dependencies between two transactions. The impact of buffer space and admission control is also not studied. Another problem with this protocol is that, in case of sequential transaction execution model, the borrower is blocked for sending the WORKDONE message and the next cohort can not be activated at other site for its execution. It will be held up till the lender completes. If its sibling is activated at another site anyway, the cohort at this new site will not get

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the result of previous site because previous cohort has been blocked for sending of WORKDONE message due to being borrower [24]. Lam et al. proposed deadline-driven conflict resolution (DDCR) protocol which integrates concurrency control and transaction commitment protocol for firm real time transactions [11]. DDCR resolves different transaction conflicts by maintaining three copies of each modified data item (before, after and further) according to the dependency relationship between the lock requester and the lock holder. This not only creates additional workload on the systems but also has priority inversion problem. The serializability of the schedule is ensured by checking the before set and the after sets when a transaction wants to enter the decision phase. The protocol aims to reduce the impact of a committing transaction on the executing transaction which depends on it. The conflict resolution in DDCR is divided into two parts (a) resolving conflicts at the conflict time; and (b) reversing the commit dependency when a transaction, which depends on a committing transaction, wants to enter the decision phase and its deadline is approaching. If data conflict occurs between the executing and committing transactions, system’s performance will be affected. Pang Chung-leung and Lam K. Y. proposed an enhancement in DDCR called the DDCR with similarity (DDCR-S) to resolve the executing-committing conflicts in DRTDBS with mixed requirements of criticality and consistency in transactions [4]. In DDCR-S, conflicts involving transactions with looser consistency requirement, the notion of similarity are adopted so that a higher degree of concurrency can be achieved and at the same time the consistency requirements of the transactions can still be met. The simulation results show that the use of DDCR-S can significantly improve the overall system performance as compared with the original DDCR approach. Based on PROMPT and DDCR protocols, B. Qin and Y. Liu proposed double space commit (2SC) protocol [15]. They analyzed and categorized all kind of dependencies that may occur due to data access conflicts between the transactions into two types commit dependency and abort dependency. The 2SC protocol allows a non-healthy transaction to lend its held data to the transactions in its commit dependency set. When the prepared transaction aborts, only the transactions in its abort dependency set are aborted and the transactions in its commit dependency set execute as normal. These two properties of the 2SC reduce the data inaccessibility and the priority inversion that is inherent in distributed real-time commit processing. 2SC protocol uses blind write model. Extensive simulation experiments have been performed to compare the performance of 2SC with that of other protocols such as PROMPT and DDCR. The simulation results show that 2SC has the best performance. Furthermore, it is easy to incorporate it in any current concurrency control protocol. Ramamritham et al. [18] have given three common types of constraints for the execution history of concurrent transactions. The paper [16] extends the constraints and gives a fourth type of constraint. Then the weak commit dependency and abort dependency between transactions, because of data access conflicts, are analyzed. Based on the analysis, an optimistic commit protocol Two-Level Commit (2LC) is proposed, which is specially designed for the distributed real time domain. It allows

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transactions to optimistically access the locked data in a controlled manner, which reduces the data inaccessibility and priority inversion inherent and undesirable in DRTDBS. Furthermore, if the prepared transaction is aborted, the transactions in its weak commit dependency set will execute as normal according to 2LC. Extensive simulation experiments have been performed to compare the performance of 2LC with that of the base protocols PROMPT and DDCR. The simulation results show that 2LC is effective in reducing the number of missed transaction deadlines. Furthermore, it is easy to be incorporated with the existing concurrency control protocols. Many existing commit protocols try to improve system performance by allowing a committing cohort to lend its data to an executing cohort, thus reducing data inaccessibility [7]. These protocols block the borrower when it tries to send WORKDONE/PREPARED message thus increasing the transactions commit time. The paper [22] first analyzes all kind of dependencies that may arise due to data access conflicts among executing-committing transactions when a committing cohort is allowed to lend its data to an executing cohort. It then proposes a static two-phase locking and high priority based, write-update type, ideal for fast and timeliness commit protocol i.e. SWIFT. In SWIFT, the execution phase of a cohort is divided into two parts, locking phase and processing phase and then, in place of WORKDONE message, WORKSTARTED message is sent just before the start of processing phase of the cohort. Further, the borrower is allowed to send WORKSTARTED message, if it is only commit dependent on other cohorts instead of being blocked as opposed to [9]. This reduces the time needed for commit processing and is free from cascaded aborts. To ensure non-violation of ACID properties, checking of completion of processing and the removal of dependency of cohort are required before sending the YES-VOTE message. Simulation has been done for main memory resident as well as disk resident databases. Simulation results show that SWIFT improves the system performance in comparison to earlier protocol. The performance of SWIFT is also analyzed for partial read-only optimization, which minimizes intersite message traffic, execute-commit conflicts and log writes resulting in a better response time. The impact of permitting the cohorts of the same transaction to communicate with each other [11] has also been analyzed. The SWIFT commit protocol is beneficial only if the database is main memory resident [22]. The concept of Shadow was proposed by Azer Bestavros. In this approach, we make a copy of original transaction known as Shadow of the original transaction. The original transaction continues to run uninterrupted, while the shadow transaction is started on a different processor and allowed to run concurrently. In other words, two versions of the same transaction are allowed to run in parallel, each one being at a different point of its execution. Obviously, only one of these two transactions will be allowed to commit; the other will be aborted. The concept of Shadow was first used for commit process in shadow PROMPT [9] which is a modification of PROMPT commit protocol. Here, a cohort forks off a replica of the transaction, called a shadow, independent of the type of dependency. Thus, in shadow PROMPT, shadow is created in case of Abort as well as Commit

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dependency. However in case of Commit dependency, there is no need to create a shadow because an abort dependent transaction will complete its execution irrespective of final outcome of lender transaction. Thus, in Shadow PROMPT [9], unnecessary shadow is created. The problem has been solved in DSS-SWIFT commit protocol [23], where a shadow is created whenever an abort dependent borrower cohort uses dirty value of a data item updated by an uncommitted lender cohort. Here, each site maintains abort dependency set (ADSS), which is the set of shadow of those cohorts that are abort dependent on lender. Also, the DSSSWIFT creates the non beneficial shadows in some cases. The SPEEDITY commit protocol [1] reduces non beneficial shadows that are created in DSS-SWIFT by the introduction of Tshad ow C reat lon T i me where the abort dependent cohort having deadline beyond a specific value (Tshad ow C reat lon T i me ) can only forks off a replica of itself called shadow, whenever it borrows dirty value of a data item. The SWIFT commit protocol typically uses S2PL mechanism to acquire locks [24]. Due to several conditional statements in the code of transaction [3], some of the locked data items may not be used at all. Hence, here in our work, these unused locked data items are being unlocked immediately after completion of processing phase. Also, a lending transaction can lend its dirty data to more than one borrower cohort in case of write-read conflicts by creating a single shadow only. The remainder of this chapter is organized as follows. Section 1.3 introduces the distributed real time database system model. Section 1.4 presents PERDURABLE and its pseudo code. Section 1.5 discusses the simulation results and Sect. 1.6, finally concludes the chapter.

1.3 DRTDBS Model In distributed database system model, the global database is partitioned into a collection of local databases stored at different sites. A communication network interconnects the sites. There is no global shared memory in the system, and all sites communicate via message exchange over the communication network. We assume that the transactions are firm real time type. Each transaction in this model exists in the form of a coordinator that executes at the originating site of the transaction and a collection of cohorts that execute at various sites, where the required data items reside. If there is any local data in the access list of the transaction, one cohort is executed locally. Before accessing a data item, the cohort needs to obtain lock on data items. Sharing of the data items in conflicting modes creates dependencies among the conflicting local transactions/cohorts, and constraints their commit order. We also assume that: • The processing of a transaction requires the use of CPU and data items located at local site or remote site. • Arrival of transactions at a site is independent of the arrivals at other sites and uses Poisson distribution.

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• Each cohort makes read and update accesses. • Each transaction pre-declares its read-set (set of data items that the transaction will only read) and write-set (set of data items that the transaction will write). • Cohorts are executed in parallel. • A distributed real time transaction is said to commit, if the coordinator has reached to the commit decision before the expiry of the deadline at its site. This definition applies irrespective of whether cohorts have also received and recorded the commit decision by the deadlines or not. • Studies have been made for both main memory resident and disk resident database. • Communication delay considered is either 0 or 100 ms. • In case of disk resident database, buffer space is sufficiently large to allow the retention of data updates until commit time. • The updating of data items is made in transaction own memory rather than in place updating.

1.4 PERDURABLE Protocol In conventional databases, the execution time and data to be accessed is not known at the time of transaction submission. However, for most well defined real time transactions, an estimate of execution time and the locks required by the transaction are known prior. Actually, many real-time concurrency control protocols have made these assumptions or even stronger assumptions. For example, priority ceiling protocols [17, 19] explicitly assume the availability of such information. The real-time S2PL (RT-S2PL) protocols do possess desirable features making them suitable for RTDBS, especially for distributed real-time database systems (DRTDBS), in which remote locking is required and distributed deadlock is possible. At the same time, the problem of prior knowledge on the required data objects of a transaction is easy to address in DRTDBS as it is generally agreed that the behavior and the data items to be accessed by real-time transactions, especially hard real-time transactions, are much more well-defined and predictable. In S2PL, each transaction pre-declares it’s read-set and write-set. If a transaction contains conditional statements, a pessimistic approach is taken and data used in all branches are included and locked irrespective of their use. Here, all the locked data items will never be used. Hence, all the locked data items by the transaction, which are still unused after completion of processing of transaction, can be unlocked after end of processing phase to reduce data contention. This is achieved by relaxing the notion of 2PL but still maintaining the database consistency. A major aim in developing a database system is to allow several users to access shared data concurrently. Concurrent access is easier if users are only reading data. Also, a lending transaction can lend its dirty data to more than one borrower cohort in read mode in case of write-read conflicts by creating only a single shadow. In case of commit of lender, the entire borrower cohorts will run independently without any

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conflict because of being read type. However, in case of abort of lender, the single shadow will be used for the survival of all the borrower cohorts. Based on these concepts, we introduce our new protocol which is enhanced version of base protocol SWIFT. Sharing of data items in conflicting modes creates dependencies among conflicting transactions and constraints their commit order. We assume that a cohort requests an update lock if it wants to update a data item x. The prepared cohorts called as lenders lend uncommitted data to concurrently executing transactions known as borrower. Here, the borrower is further divided into two categories. • Commit Dependent Borrower • Abort Dependent Borrower Therefore, modified definitions of dependencies used in this chapter are given below. • Commit dependency (CD). If a transaction T2 updates a data items read by another transaction T1 a commit dependency is created from T2 to T1 . Here, T2 is called as commit dependent borrower and is not allowed to commit until T1 commits. • Abort dependency (AD). If T2 reads/updates an uncommitted data item updated by T1 , an abort dependency is created from T2 to T1 . Here, T2 is called as abort dependent borrower. T2 aborts, if T1 aborts and T2 is not allowed to commit before T1 . Each transaction/cohort Ti that lends its data while in prepared state to an executing transaction/cohort Tj , maintains two sets. • Commit Dependency Set CDS .Ti /: set of commit dependent borrower Tj , that are borrowed dirty data from lender Ti . • Abort Dependency Set ADS .Ti /: the set of abort dependent borrower Tj that are borrowed dirty data from lender Ti . These dependencies are required to maintain the ACID properties of the transaction. Each lender is associated with a health factor defined as follows. HF (health factor) = TimeLeft/MinTime where TimeLeft is the time left until the transaction’s deadline, and MinTime is the minimum time required for commit processing. The health factor is computed at the time when the coordinator is ready to send the YES-VOTE messages. MinHF is the threshold that allows the data held by committing transaction to be accessed. The variable MinHF is the key factor to influence the performance of the protocol. In our experiments, we have taken MinHF as 1.2, the value of MinHF used in PROMPT [9]. Here, each transaction also maintains shadow set (DS), which is the set of shadows of those cohorts that are abort dependent on lender. DS (Ti /: Shadow of those cohorts which are in the abort dependency set of Ti:

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Hence, if T2 is abort dependent on T1 then Shadow of T2 , S2 is created in DS (T1 /. If T2 accesses a data item already locked by T1 ; the lock manager processes the data item accesses in conflicting mode as follows. if(T1 has not lent the data item) f If (T2 CD T1 / f CDS (T1 / = CDS (T1 fT 2g; T2 is granted Update lock; g else if ((T2 AD T1 AND (HF(T1 > MinHF)) f ADS (T1 / = ADS (T1 fT 2g; T2 is granted the requested lock; Add shadow of T2 in DS(S); g else T2 will be blocked; g else f if (T2 is read type) f if ((T2 AD T1 AND (HF(T1 > MinHF)) f ADS (T1 = ADS (T1 fT 2g; T2 is granted the requested lock; g else T2 will be blocked; g g

1.4.1 Type of Dependency in Different Cases of Data Conflicts When data conflicts occur, there are three possible cases of conflicts. Case 1: Read-Write Conflict If T2 requests update-lock while T1 is holding a read-lock, a commit dependency is defined from T2 to T1 . The transaction id of T2 is added to the CDS (T1 /. Then, T2 acquires the update-lock. Case 2: Write-Write Conflict. If both locks are update-locks and HF(T1 > MinHF, an abort dependency is defined from T2 to T1 . The transaction id of T2 is added to ADS (T1 ). T2 acquires the update-lock; otherwise, T2 is blocked. In case of getting update-lock, the shadow of T2 is created and added to DS (T1 /.

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Case 3: Write-Read Conflict When data conflicts occur, there are two possible situations of conflict. Situation 1W T1 has not lent the data items. If T2 requests a read-lock while T1 is holding an update-lock and HF (T1 > MinHF), an abort dependency is defined from T2 to T1 . The transaction id of T2 is added to ADS (T1 /. T2 acquires the read-lock; otherwise, T2 is blocked. In case of getting read-lock, the shadow of T2 is created and added to DS (T1 /. Situation 2W T1 has lent the data items. If T2 requests a read-lock while T1 is holding an update-lock and HF (T1 > MinHF), an abort dependency is defined from T2 to T1 . The transaction id of T2 is added to ADS (T1 /. T2 acquires the read-lock; otherwise, T2 is blocked.

1.4.2 Mechanics of Interaction Between Lender and Borrower Cohorts If T2 accesses a data item already locked by Tb one of the following three scenarios may arise. Scenario 1: T1 receives decision before T2 has completed its local data processing: If the global decision is to commit, T1 commits. All cohorts in ADS (T1 / & CDS (T1 / will execute as usual; Sets of ADS (T1 /, CDS (T1 / & DS (T1 / will be deleted; If the global decision is to abort, T1 aborts, cohorts in the dependency set of T1 will execute as follows: Shadows of all cohorts abort dependent on T1 in DS(S) will be activated and sends YES-VOTE piggy bagged with the new result to their coordinator, only if they can complete execution; otherwise, discarded; Transactions in CDS (T1 / will execute normally; Delete Set ADS (T1 /, DS (T1 / and CDS (T1 / CDS (T1 /; Scenario 2: T2 is about to start processing phase after getting all its locks before T1 receives global decision. T2 sends WORKSTARTED message to its master and start processing phase. After end of processing phase, all the unused data items are released. Scenario 3: T2 aborts before, T1 receives decision In this situation, T2 ’s updates are undone and T2 is removed from the dependency set of T1 .

1.4.3 Algorithm On the basis of above discussions, the complete pseudo code of the protocol is given below. if (T1 receives global decision before T2 ends processing) f One: if (T1 ’s global decision is to commit)

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f T1 enters in the decision phase; Delete Set ADS (T1 /, DS (T1 / and CDS (T1 /;

g else if (T1 ’s global decision is to abort) f T1 aborts; Transactions in CDS (T1 / will execute as usual. For all the abort dependent cohorts f if (shadow) f if (Shadow of cohort can complete its execution) Execute Cohorts Shadow in DS (T1 / and send YES-VOTE piggy bagged with the new result; else discard shadow; g g Delete Set ADS (T1 /, DS (T1 / and CDS (T1 /; g g else f if (T2 aborted by higher transaction before T1 receives decision OR T2 expires its deadline) f Undo the computation of T2 ; Abort T2 ; Delete T2 from ADS (T1 /, CDS (T1 /; if (shadow) Delete T2 from DS (T1 /; g else if (T2 ends executing phase before T1 receives global decision) T2 sends WORKSTARTED message & start processing phase. After end of processing phase, all the unused data items are released. g Main Contributions • A lender already lent data to abort dependent borrower cohort can lend the same data item in read mode to more than one borrower cohort. • Locked data items, which are not used during processing phase, can be released even before commit phase. Correctness To maintain ACID property of database, cohort sends the YES-VOTE in response to its coordinator’s VOTE-REQ message only when its dependencies are removed

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and it has finished its processing [20, 22]. An atomic commitment protocol ensures that the cohorts in a transaction agree on the outcome, i.e. ABORT or COMMIT. Each participant vote, YES or NO, on whether they can guarantee the local ACID properties of the transaction. All participants have a right to veto the transaction, thus causing it to abort. The proposed protocol has these properties: AC1 huniform agreementi All cohorts that decide reach the same decision. AC2 hintegrityi A cohort cannot reverse its decision after it has reached one. AC3 huniform validityi COMMIT can only be reached if all cohort voted YES. AC4 hnon-trivialityi If there are no failures and no cohort voted NO, then the decision will be to COMMIT. AC5 hterminationi Every cohort eventually decides.

1.5 Performance Measures and Evaluation Transactions are characterized by the amount of data they access, the nature of the accesses (read or write), the priority assigned to them, their deadline and so on. Since no meaningful comparison of the algorithms from the different camps is possible and even within the single camp, it is often difficult to compare different algorithms; they were chosen to be in accordance with those used in earlier published simulation studies. The default values of different parameters for simulation experiments are same as in [27] and are given in Table 1.1. The transaction arrival process is assumed to be Poisson with parameter inter-arrival time. Also, to evaluate the Cubic effect of transaction length, range was taken from 3–20 being much more variable in nature which is really a difficult task. Transaction have extensive data resource requirements which are either pre-declared before execution or more often acquired at the record level as the transaction progress. In conventional database systems, the major performance measures are mean system throughput and mean system response time. On the contrary, minimizing the number of missed deadlines is the main concern in DRTDBS. Research work on real time locking is mainly based on D2PL. But, there is no strong evidence till now that the performance of real-time D2PL based protocols is better than those based on Table 1.1 Default values for model parameters Parameters Nsite AR Tcom SF Noper PageCPU PageDisk DBsize Pwrite

Meaning Number of site Transaction arrival rate Communication delay Slack factor No. of operations in a transaction CPU page processing time Disk page processing time Database size Write operation probability

Default setting 4 4 Transactions/ Second 100 ms (Constant) 1–4 (Uniform distribution) 3–20 (Uniform distribution) 5 ms 20 ms 200 Data objects/Site 0.60

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S2PL. As a result of the better defined nature of real time transactions, it is not unreasonable to assume that the locking information of transactions is known before processing. Hence, the concurrency control scheme used is static two phase locking with higher priority. Miss Percentage (MP) is the primary performance measure used in the experiments and is defined as the percentage of input transactions that the system is unable to complete on or before their deadlines [9]. Since mathematical modeling makes several crude approximations so it reasonable required verifying the performance of transaction behavior with help of discrete event simulation. Since there were no practical benchmark programs available in the market or with research communities to evaluate the performance of protocols and algorithms, an event driven based simulator was written in C language. In our simulation, a small database (200 data items) is used to create high data contention environment. This helps us in understanding the interaction among the different policies [10]. Transactions make requests for data items and concurrency control is implemented at data item level. A small database means that degree of data contention in the system can easily be controlled by the sizes of the transactions. A small database also allows us to study the effect of hot-spots, in which a small part of the database is accessed very frequently by most of the transactions. However, we have not considered the hot spot issue. The write operation probability Pwri t e parameter determines the probability that a transaction operation is a write. This is very important as real time database systems have applications in highly critical environment where the performance of system is important especially in highly loaded environment. For each set of experiment, the final results are calculated as an average of 10 independents runs. In each run, 100,000 transactions are initiated.

1.5.1 Simulation Results In this section we present the performance results from our experiments comparing the various commit protocols using the simulation model. The model consists of a non replicated firm deadline database system that is distributed over a set of sites connected by a network. Simulation was done for both the main memory resident and the disk resident databases at communication delay of 0ms & 100ms. Also, the write probability is 0.60% and approximately 20%–25% data items are not being use by the transaction during processing phase. The settings of the workload parameters were chosen with the objective of having significant data contention thus helping to bring out the performance differences between the various commit protocols. Our experiments consider only data contention because resource contention can usually be addressed by purchasing more and/or fast resources while it is not easily possible with data contention. We compared PERDURABLE with PROMPT, 2SC and SWIFT in this experiment. Figures 1.1–1.5 show the Miss Percent behavior under normal and heavy load conditions with/without communication delay for main memory resident database system and disk resident database system. In these graphs, we first observe that there

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is a noticeable difference between the performances of the various commit protocols throughout loading range. This is due to increased availability of data items and allowing multiple cohort/transactions simultaneously with single backup shadow. Here, cohorts are able to obtain lock sooner than base protocols. Hence, the cohorts, who are being aborted due to deadline expiry, tend to make further progress. In this way, we can reduce the data contention and increase system throughput by allowing more cohort/transactions to be carried out at a time. Sharing of data items in conflicting mode creates dependencies among conflicting transactions and constraints their commit order. It is assumed that a cohort requests an update lock if it wants to update a data item x. The prepared cohorts, called as lenders, lend uncommitted data to concurrently executing transactions known as borrower. If a cohort fork off a replica of the transaction, it is called as shadow. The original incarnation of the transaction continues its execution, while the shadow is blocked after finishing its execution. If the lender finally commits, the borrower continues its on-going execution and the shadow is discarded; otherwise, borrower is aborted due to abort of lender and shadow is activated. Here, we maintain a single backup shadow for multiple transactions compared to creating a separate shadow for each cohort/transaction in previous real time commit protocols with still limiting the transaction abort chain to one only and reducing the data inaccessibility. Also, the resources in creating these backup shadows for each cohort are saved. The overhead incurred due to shadow creation is also minimizes. This combines the advantages of both pessimistic as well as optimistic approaches. Here, transaction is restarted from the intermediate conflict point (not from beginning) giving it more chance to meet its deadline. Although transaction class pre-analysis is considered too restrictive in many conventional environments, it is perfectly valid in time critical environments in which all application code is pre-analyzed. Hence, by releasing the locked data items which are unused at the end of processing phase, we are making them available all through commit phase which is usually one-third of total processing time. In fact, the behaviour of all the protocol at normal load is almost similar. However, this is basically beneficial in overload situation and also this is particularly true for multiprocessor environment, in which the data not the CPU is the limiting resource. With this approach, the proposed protocol overcomes the demerits of S2PL to some extents by not locking the data items for longer period with deadlock free environment in overload situation. A prototype system was developed over which we have implemented our protocol and other protocols (PROMPT, 2SC, SWIFT, etc.) to view the performance of these protocols in the real life applications also. For testing the system, we have used total 15 nodes (2.2 Core 2 Due processor, 1 GB RAM, 160 GB HDD, Windows-XP and three servers). Still, the PERDURABLE commit protocol provides a performance that is significantly better than other commit protocols and be implemented easily by integrated with 2PC protocol by allowing transactions to access uncommitted data-item held by prepared transactions in the optimistic belief that this data-item will eventually be committed.

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Main Memory Resident Database 80 PROMPT 2SC SWIFT PERDURABLE

Miss %

60

40

20

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80 60 40 20 0 2

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1.6 Conclusion While real time scheduling of transactions are widely studied in a real time databases, the issues of conditional statements and shadowing has not yet been addressed more. Motivated by this observation, we introduced a new commit protocol PERDURABLE, where a lender already lent data to abort dependent borrower cohort can lend the same data item in read mode to more than one borrower cohort and locked data items, which are not used during processing phase, can be released even before commit phase. To the best of our knowledge, such study has not been attempted before. The main conclusion of our study can be summarized as follow. We compared the proposed protocol with base protocols. Our performance results clearly establish the superiority of PERDURABLE significantly over the standard algorithms for all the workloads and system configurations considered in this study. Here, the new notion has solved the problem of transactions holding locks for a longer time with static locking, because transactions must acquire all their locks before they can begin execution. As future work, it is desirable to study the performance of the proposed protocol in real environment more extensively.

References 1. Agrawal, S., Singh, A.N., Anand, A., Shanker, U.: SPEEDITY-A real time commit protocol. Int. J. Comput. Appl. 3(13) (2010) 2. Attaluri, G.K., Kenneth, S.: The presumed-either two-phase commit protocol. IEEE Trans. Knowl. Data. Eng. 14(5), 1190–1196 (2002) 3. Buchman, A.P., McCarthy, D.R., Hsu, M., Dayal, U.: Time Critical Database Scheduling: A Framework for Integrating Real Time Scheduling and Concurrency Control. IEEE Fifth International Conference on Data Engineering, pp. 470–480, Los Angeles, California (1989) 4. Chung-Leung, P., Lam, K.Y.: On Using Similarity for Resolving Conflicts at Commit in Mixed Distributed Real-Time Databases. Proceedings of the 5th International Conference on RealTime Computing Systems and Applications (1998) 5. Gray, J.: Notes on database operating systems: an advanced course. Lecture Notes in Computer Science, Vol. 60, pp. 397–405, Springer, Verlag (1978) 6. Gray, J., Reuter, A.: Transaction Processing: Concepts and Technique. Morgan Kaufman, San Mateo, CA (1993) 7. Gupta, R., Haritsa, J.R., Ramamritham, K., Seshadri, S.: Commit processing in distributed real time database systems. In: Proceedings of Real-time Systems Symposium, IEEE Computer Society Press, Washington DC, San Francisco (1996) 8. Gupta, R., Haritsa, J.R., Ramamritham, K.: More optimistic about real time distributed commit processing. In: Proceedings of Real-Time Systems Symposium (1997) 9. Haritsa, J., Ramamritham, K., Gupta, R.: The PROMPT real time commit protocol. IEEE Trans. Parallel Distr. Syst. 11(2), 160–181 (2000) 10. Lam, K.-Y.: Concurrency Control in Distributed Real – Time Database Systems, PhD Thesis, City University of Hong Kong, Hong Kong (1994) 11. Lam, K.Y., Pang, C.-L., Son, S.H., Cao, J.: Resolving executing-committing conflicts in distributed real-time database systems. Comput. J. 42(8), 674–692 (1999)

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12. Lee, I., Yeom, H.Y.: A Single Phase Distributed Commit Protocol for Main Memory Database Systems. 16th International Parallel & Distributed Processing Symposium (IPDPS 2002), Ft. Lauderdale, Florida, USA (2002) 13. Misikangas, P.: 2PL and its variants. Seminar on Real-Time Systems, Department of Computer Science, University of Helsinki (1997) 14. Mohan, C., Lindsay, B., Obermarck, R.: Transaction management in the R* distributed database management system. ACM Trans. Database Syst. 11(4), 378–396 (1986) 15. Qin, B., Liu, Y.: High performance distributed real time commit protocol. J. Syst. Software, Elsevier Science Inc, 1–8 (2003) 16. Qin, B., Liu, Y., Yang. J.C.: A commit strategy for distributed real-time transaction. J. Comput. Sci. Tech. 18(5), 626–631 (2003) 17. Rajkumar, R.: Task Synchronization in real time systems. Ph.D. Thesis, Carnegie-Mellon University (1989) 18. Ramamritham, K., Chrysanthis, P.K.: A taxonomy of correctness criteria in database applications. VLDB Journal, 5, 85–97 (1996) 19. Sha, L., Rajkumar, R., Lehoczky, J.P.: Concurrency control for distributed real time data bases. ACM SIGMOD Record 17(1), 82–98 (1988) 20. Shanker, U., Misra, M., Sarje, A.K.: Dependency sensitive distributed commit protocol. In: Proceedings of the 8th International Conference on Information Technology (CIT 05), pp. 41–46, Bhubaneswar, India (2005) 21. Shanker, U., Misra, M., Sarje, A.K.: Some performance issues in distributed real time database systems. In: Proceedings of the VLDB PhD Workshop (2006), the Convention and Exhibition Center (COEX), Seoul, Korea (2006) 22. Shanker, U., Misra, M., Sarje, A.K.: SWIFT-A new real time commit protocol. Int. J. Distrib. Parallel Databases 20(1), 29–56 (2006) 23. Shanker, U., Misra, M., Sarje, A.K., Shisondia, R.: Dependency sensitive shadow SWIFT. In: Proceedings of the 10th International Database Applications and Engineering Symposium (IDEAS 06), pp. 373–276, Delhi, India (2006) 24. Shanker, U., Misra, M., Sarje, A.K.: Distributed real time database systems: background and literature review. Int. J. Distrib. Parallel Databases 23(2), 127–149 (2008) 25. Skeen, D.: Nonblocking commit protocols. In: Proceedings of the 1981 ACM SIGMOD International Conference on Management of Data, pp. 133–142, New York (1981) 26. Soparkar, N., Levy, E.H., Korth, F., Silberschatz, A.: Adaptive Commitment for RealTime Distributed Transaction. Technical Report TR-92-15, Department of Computer Science, University of Texax, Austin (1992) 27. Ulusoy, O.: Concurrency Control in Real Time Database Systems. PhD Thesis, Department of Computer Science, University of Illinois Urbana-Champaign (1992)

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Chapter 2

Dimension Table Selection Strategies to Referential Partition a Fact Table of Relational Data Warehouses Ladjel Bellatreche

2.1 Introduction Enterprise wide data warehouses are becoming increasingly adopted as the main source and underlying infrastructure for business intelligence (BI) solutions. Note that a data warehouse can be viewed as an integration system, where data sources are duplicated in the same repository. Data warehouses are designed to handle the queries required to discover trends and critical factors are called Online Analytical Processing (OLAP) systems. Examples of an OLAP query are: Amazon (www. amazon.com) company analyzes purchases by its customers to come up with an individual screen with products of likely interest to the customer. Analysts at WalMart (www.walmart.com) look for items with increasing sales in some city. Star schemes or their variants are usually used to model warehouse applications. They are composed of thousand of dimension tables and multiple fact tables [15, 18]. Figure 2.1 shows an example of star schema of the widely-known data warehouse benchmark APB-1 release II [21]. Here, the fact table Sales is joint to the following four dimension tables: Product, Customer, Time, Channel. Star queries are typically executed against the warehouse. Queries running on such applications contain a large number of costly joins, selections and aggregations. They are called mega queries [24]. To optimize these queries, the use of advanced optimization techniques is necessary. By analyzing the most important optimization techniques studied in the literature and supported by the most important commercial Database Management Systems (DBMS) like Oracle, SQL Server, DB2, etc. we propose to classify them into two main categories: (1) optimization techniques selected during the creation of data warehouses and (2) optimization techniques selected after the creation of the warehouses. The decision of choosing techniques in the first category is

L. Bellatreche () LISI/ENSMA – Poitiers University, 1, avenue Cl´ement Ader, 86960 Futuroscope, France e-mail: [email protected] ¨ T. Ozyer et al. (eds.), Recent Trends in Information Reuse and Integration, DOI 10.1007/978-3-7091-0738-6 2, © Springer-Verlag/Wien 2012

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Fig. 2.1 Logical schema of the data warehouse benchmark APB-1 release II

taken before creating the data warehouse. Horizontal partitioning of tables and parallel processing are two examples of the first category. Techniques belonging to the second category are selected during the exploitation of the data warehouse. Examples of these techniques are materialized views and indexes, data compression, etc. Based on this classification, we note that the use of optimization techniques belonging to the first category is more sensitive compared to those belonging to the second one, since the decision of using them is usually taken at the beginning stage of the data warehouse development. To illustrate this sensitivity, imagine the following two scenarios: (1) a database administrator (DBA) decides to partition her/his warehouse using a native data definition language (DDL) proposed by his/her favorite DBMS, later on, DBA realizes that data partitioning is not well adapted for his/her data warehouse. Therefore, it will be costly and time consuming to reconstitute the initial warehouse from the partitions. (2) DBA selects indexes or materialized views (techniques belonging to the second category) and if she/he identifies their insufficiencies to optimize queries, she/he can easily drop or replace them by other optimization techniques. This sensitivity and the carefulness of optimization techniques belonging to the first category motivate us to study one of them, known as referential horizontal partitioning. Horizontal partitioning is one of important aspect of physical design [23]. It is a divide-and-conquer approach that improves query performance, operational scalability, and the management of ever-increasing amounts of data [28]. It divides tables/views into disjoint sets of rows that are physically stored and accessed separately. Unlike indexes and materialized views, it does not replicate data, thereby reducing storage requirement and minimizing maintenance overhead. Most of today’s DBMSs (Oracle, DB2, SQL Server, PostgreSQL, Sybase and MySQL) offer native Data Definition Language support for creating partitioned tables (Create Table ... Partition) and manipulating horizontal partitions (Merge and Split) [9, 20, 23]. Partitioning is not restricted to data tables. Indexes may also be

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partitioned. A local index may be constructed so that it reflects the structure of an underlying table [23]. Notice that when horizontal partitioning is applied on materialized views and indexes, it will be considered as a technique of the second category. Its classification depends on the nature of the used access method (table, materialized view or index). Horizontal partitioning improves performance of queries by the mean of pruning mechanism that reduces the amount of data retrieved from the disk. When a partitioned table is queried, the database optimizer eliminates non relevant partitions by looking the partition information from the table definition, and maps that onto the Where clause predicates of the query. Two main pruning are possible: partition pruning and partition-wise join pruning [9]. The first one prunes out partitions based on the partitioning key predicate. It is used when only one table is partitioned. The second one prunes partitions participating in a join operation. It is applicable when the partitioning key is same in the two tables. Two main types of horizontal partitioning exist [5]: mono table partitioning and table-dependent partitioning. In the mono table partitioning, a table is partitioned using its own attributes. It is quite similar to the primary horizontal partitioning proposed in traditional databases [22]. Several modes exist to support mono table partitioning: Range, List, Hash, Round Robin (supported by Sybase), Composite (Range-Range, Range-List, List-List), etc.), Virtual Column partitioning recently proposed by Oracle11G, etc. Mono table partitioning may be used to optimize selections, especially when partitioning key matches with their attributes (partition pruning). In the data warehouse context, it is well adapted for dimension tables. In table-dependent partitioning, a table inherits the partitioning characteristics from other table. For instance a fact table may be partition based on the fragmentation schemes of dimension tables. This partitioning is feasible if a parent-child relationship among these tables exists [7, 9]. It is quite similar to the derived horizontal partitioning proposed in 1980s in the context of distributed databases [7]. Two main implementations of this partitioning are possible: native referential partitioning and simulated referential partitioning. The native referential partitioning is recently supported by Oracle11G to equi-partition tables connected by a parent child referential constraint. A native DDL is given to perform this partitioning [9] (Create Table ... Partition by Reference ...). It optimizes selections and joins simultaneously. Therefore, it is well adapted for star join queries. Simulated referential partitioning is a manual implementation of referential partitioning, i.e., it simulates it using the mono table partitioning mode. This simulation may be done as follows: (1) a dimension table is first horizontally partitioned using its primary key, then the fact table is decomposed based on its foreign key referencing that dimension table. This partitioning has been used for designing parallel data warehouses [10], but it is not well adapted for optimizing star join queries [9]. Figure 2.2 classifies the proposed partitioning modes. By analyzing the most important works done on referential partitioning in the data warehousing environment, we draw the two following points: (a) most of proposed algorithms control the number of generated fragments of the fact tables in order to facilitate the manageability of the data warehouse [2, 16, 25] and

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L. Bellatreche Horizontal Partitioning

Dependent Table Partitioning

Mono Table Partitioning

Simple Mode

Range

List

...

Composite Mode

Hash Range-Range

...

Native Referential Partitioning

User-Driven Referential Partitioning

List-Hash

Fig. 2.2 A classification of the proposed partitioning modes

(b) the fragmentation algorithms start from a bag containing all selection predicates involved by the queries. Therefore, dimension tables referenced by these predicates have the same chance to be used to referential partition a fact table of a warehouse. This situation is not always the best choice to optimize queries. This finding is based on the following observation: To execute a star join query involving several tables in a non partitioned data warehouse, the query optimizer first shall establish an order of joins [26]. This order should reduce the size of intermediate results. To execute the same query on a horizontally partitioned warehouse, the query optimizer will do the same task as in the non partitioned scenario. The join order is somehow partially imposed by the fragmentation process, since fragments of the fact table will be first joined with the fragments of a dimension table used in the referential partitioning process in order to perform partition-wise join pruning. Based on this analysis, we notice that the choice of dimension table(s) used to partition the fact table is a crucial performance issue that should be addressed. Contrary to the existing horizontal partitioning approaches that consider all dimension tables involved by the queries, we propose to identify relevant dimension table(s) and then generate fragmentation schemes using existing algorithms. This partitioning manner will reduce the complexity of the partitioning problem which is known as a NP-complete problem [3], since selection predicates belonging to non desirable dimension table(s) will be discarded. Note that the complexity of horizontal partitioning is proportional to the number of predicates [22]. For data warehouse applications managing a reasonable number of dimension tables, DBA may identify manually dimension tables based on her/his experience, but for BI applications, where hundred of dimension tables are used, it will be hard to do it manually. Therefore, the development of automatic techniques offering DBA the possibility to select dimension tables to partition her/his fact table is necessary. In this paper, we propose a comprehensive procedure for automatically selecting dimension table(s) candidate to referential partition a fact table. This paper is divided in six sections: Section 2.2 reviews the existing works on horizontal partitioning in traditional databases and data warehouses. Section 2.3 proposes selection strategies of dimension table(s) to support referential horizontal partitioning. Section 2.4 gives the experimental results done on the data set of the APB-1 benchmark using a mathematical cost model and on Oracle11. Section 2.5,

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a tool supporting our proposal is given. It is connected to Oracle11G DBMS. Section 2.6 concludes the paper by summarizing the main results and suggesting future work.

2.2 Related Work Many research works dealing with horizontal partitioning problem were proposed in traditional databases and data warehouses. In the traditional database environment, researchers concentrate their efforts on proposing algorithms for the mono table partitioning. These algorithms are guided by a set of selection predicates of queries involving the table to be partitioned. A selection predicate is defined as follows: attribute  value, where attribute belongs to the table to be fragmented,  2 f>; >; D; ; g and value 2 domain of attribute. In [3], a classification of these algorithms is proposed: minterm generation-based approach [22], affinity-based approach [13] and cost-based approach [4]. Online analysis applications characterized by their complex OLAP queries motivate the data warehouse community to propose table-dependent horizontal partitioning methodologies and algorithms. [19] proposed a methodology to partition a relational data warehouse in a distributed environment, where the fact table is referentially partitioned based on queries defined on all dimension tables. [17] proposed a fragmentation methodology for a multidimensional warehouse, where the global hypercube is divided into sub cubes. Each one contains a sub set of data. This process is defined by slice and dice operations (similar to the selection and the projection in relational databases). Relevant dimensions to partition the hyper cube are chosen manually. In [3], a methodology and algorithms dealing with partitioning problem in relational warehouses are given. In this work, the authors propose a fragmentation methodology of the fact table based on the fragmentation schemas of dimension tables. Each dimension table involved in any selection predicate is a candidate to referential partition the fact table. Three algorithms selecting fragmentation schemes reducing query processing cost were proposed [3]: genetic, simulated annealing and hill climbing. These algorithms control of generated fragments of a fact table. The main characteristic of these algorithms is that they use a cost model to evaluate the quality of the obtained fragmentation schemes. In [5], a preliminary work on selecting dimension table(s) to partition a relational fact table is presented. In [16], an approach for fragmenting XML data warehouses is proposed. It is characterized by three main steps: (1) extraction of selection predicates from the query workload, (2) predicate clustering and (3) fragment construction with respect to predicate clusters. To form clusters of predicates, the authors proposed to use the k-means technique. As in [3], this approach controls the number of fragments. [23] showed the need of integrating horizontal and vertical partitioning in physical database design, but they did not present algorithm to support referential partitioning of a data warehouse.

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L. Bellatreche

To summarize, most important research efforts on horizontal partitioning in the traditional database are focused on the mono table partitioning, whereas in data warehouses, the efforts are concentrated on dependent table partitioning. Proposed algorithms on fragmenting a fact table consider whether all dimension tables of a data warehouse schema or a set of these tables identified by DBA. The choice of these dimension tables to partition a fact table is a crucial issue for optimizing OLAP queries. Unfortunately, the problem of selecting dimension table(s) to partition the warehouse is not well addressed; except the work done by [17] that pointed out the idea of choosing dimension to partition a hyper cube, without giving strategies to perform this selection.

2.3 Selection of Dimension Tables In this section, we propose first a formalization of the problem of selecting dimension tables and then strategies aiding DBA in choosing her/his relevant dimension tables used to referential partition a fact table.

2.3.1 Formalization of the Problem Before proposing this methodology, we formalize the referential horizontal partitioning problem as follows: Given a data warehouse with a set of d dimension tables D D fD1 ; D2 ; : : : ; Dd g and a fact table F , a workload Q of queries Q D fQ1 ; Q2 ; : : : ; Qm g and a maintenance constraint W fixed by DBA that represents the maximal number of fact fragments that he/she can maintain. The referential horizontal partitioning problem consists in (1) identifying candidate dimension table(s), (2) splitting them using single partitioning mode and (3) using their partitioning schemes to decompose the fact table into N fragments, such that: (a) the cost of evaluating all queries is minimized and (b) N  W . Based on this formalization, we note that referential partitioning problem is a combination of two difficult sub problems: identification of relevant dimension tables and their fragmentation schema selection (which is known as a NP-complete problem [2]). In the next section, we study the complexity of the first sub problem (identification of relevant dimension tables).

2.3.2 Complexity of Selecting Dimension Tables The number of possibilities that DBA shall consider to partition the fact table using referential partitioning is given by the following equation:       d d d (2.1) D 2d  1 C ::: C C d 2 1

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Example 1. Let us suppose a warehouse schema with three dimension tables Customer, Time and Product and one fact table Sales. Seven scenarios are possible to referential partition Sales: Sales(Customer), Sales(Time), Sales(Product), Sales (Customer, Time), Sales (Customer, Product), Sales (Product, Time) and Sales (Customer, Time, Product). For a data warehouse schema with a large number of dimension tables, it will be hard for DBA to choose manually relevant dimension table(s) to partition the fact table and guarantying a reduction of query processing cost. For instance, the star schema of Sloan Digital Sky Survey database, which is a real-world astronomical database, running on SQL Server has 18 dimension tables [12]. In this context, DBA needs to evaluate 218  1 (D262 143) possibilities to partition her/his fact tables. This formalization and real partitioning scenario motivate us to propose strategies aiding DBA to select relevant dimension tables.

2.3.3 Dimension Table Selection Strategies to Split the Fact Table Referential partitioning is proposed essentially to optimize join operations and facilitate the data manageability [9, 23]. Therefore, any selection strategy should hinge on join operation which well present in OLAP queries. 1. Largest dimension table: a simplest way to select a dimension table to referential partition the fact table is to choose the largest dimension table. This selection is based on the following observation: joins are typically expensive operations, particularly when the relations involved are substantially larger than main memory [14]. 2. Most frequently used dimension table: the second naive solution is to choose the most frequently used dimension table(s). The frequency of a dimension table Di is calculated by counting its appearance in the workload. These two naive strategies are simple to use. But, they ignore several logical and physical parameters like size of tables, buffer size, size of intermediate results of joins that has a real impact on join cost, etc. Generally, when query involves multiple joins (which is the case of star join queries), the cost of executing the query can vary dramatically depending on the query evaluation plan (QEP) chosen by the query optimizer. The join order and access methods used are important determinants of the lowest cost QEP [27]. The query optimizer estimates the intermediate results and uses this information to choose between different join orders and access methods. Thus, the estimation of join result sizes in a query is an important problem, as the estimates have a significant influence on the QEP chosen. Based on these observations, the choice of dimension table(s) should take into account these the sizes of intermediate results. 3. Minimum share: in data warehousing, every join operation involves the fact table. The query graph of such a query contains a root table (representing the fact table)

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a

b D1

D2

F

D3

Dd

F

D1

D3

D2

Fig. 2.3 (a) Star Query Graph, (b) Star query execution using left deep tree

and peripheral tables (representing dimension tables) (Fig. 2.3a). To optimize the execution of a star join query, an optimal sequence in which the tables are joined shall be identified. This is problem is known as join ordering [26]. Intuitively, this problem is quite similar to dimension table selection problem. Join ordering is specific to one query (Fig. 2.3b). An important difference between these two problems is that join order concerns only one query, but dimension table selection problem concerns several queries running on data warehouse. As in join ordering problem [26], the best choice of dimension table should reduce the size of intermediate results of the most important join star queries. Based on this analysis, we propose a strategy to referential partition the fact table reducing the size of intermediate results. The size (in terms of number of tuples) of join between the fact table F and a dimension table Di (which is a part of a star join query), denoted by kF ‰ Di k is estimated by: kF ‰ Di k D kDi k  Share.F:Ai /

(2.2)

where kDi k and Share.F:Ai / represent respectively, the cardinality of dimension table Di and the average number of tuples of the fact table F that refer to the same tuple of the dimension table Di via the attribute Ai . To reduce the intermediate result of a star join query, we should choose the dimension table with a minimum share. If we have a star join query involving several dimension tables, a simplest join order is to use the dimension table with a minimum share to ensure a reduction of the intermediate result. Therefore, we propose to choose the dimension table with a minimum share to referential partition the fact table. Several studies were proposed to estimate the share parameter, especially in object oriented databases [8].

2.3.4 Referential Partitioning Methodology Now, we have all ingredients to propose our methodology offering a real utilization of data partitioning in data warehouse application design. It is subdivided into three steps described in Fig. 2.4:

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Queries

Dimension table Selection

Fragmentation Attribute Selection

Fragmentation Algorithm

Cost Model

Output: Fragmentation Schema

Fig. 2.4 A methodology to support referential partitioning

1. Selection of dimension table(s) used to decompose the fact table. This selection may be done using one of the previous criteria. 2. Extraction of candidate fragmentation attributes (key partitioning). A fragmentation attribute is an attribute used by a selection predicate defined on a chosen dimension table. 3. Partition each dimension table using single table partitioning type. To generate partitioning schemes of chosen dimension tables, DBA may use his/her favourite algorithm. The complete generation of fragmentation schemes (of fact and dimension tables) is ensured by using a particular coding [2]. Each fragmentation schema is represented using a multidimensional array, where each cell represents a domain partition of a fragmentation schema. To illustrate this coding, let us suppose the following scenario: DBA choose dimension table Customer and Time to referential partition the fact table Sales. Three attributes: Age, Gender, Season, where Age and Gender belong to Customer dimension table, whereas Season belongs to Time. The domain of these attributes are: Dom.Age/ D Œ0; 120, Dom.Gender/ D f‘M’, ‘F’g, and Dom.Season/ D f“Summer”, “Spring”, “Autumn”, “Winter”g. DBA proposes an initial decomposition of domains of these three attributes as follows: Dom.Age/ D d11 [ d12 [ d13 , with d11 D Œ0; 18, d12 D 18; 60Œ, d13 D Œ60; 120. Dom.Gender/ D d21 [d22 , with d21 D f‘M 0 g, d22 D f‘F 0 g. Dom.Season/ D d31 [

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L. Bellatreche d11

d12

d13

Age 0

18

60

‘M’

‘F’

‘Summer’

‘Spring’

120

Gender

‘Autumn’

‘Winter’

Season

Fig. 2.5 Decomposition of attribute domains Fig. 2.6 Coding of a fragmentation schema

1

2

Gender

1

2

Season

1

2

Age

3 3

4

d32 [d33 [d34 , where d31 D f“Summer”g, d32 D f“Spring”g, d33 D f“Autumn”g, and d34 D f“Winter”g. Sub domains of all three fragmentation attributes are represented in Fig. 2.5. Domain partitioning of different fragmentation attributes may be represented by multidimensional arrays, where each array represents the domain partitioning of a fragmentation attribute. The value of each cell of a given array representing an attribute belongs to .1::ni /, where ni represents the number of sub domain of the attribute (see Fig. 2.6). Based on this representation, fragmentation schema of each dimension table Dj is generated as follows: • All cells of a fragmentation attribute of Dj have different values: this means that all sub domains will be used to partition Dj . For instance, the cells of each fragmentation attribute in Fig. 2.5 are different. Therefore, they all participate in fragmenting their corresponding tables (Customer and Time). The final fragmentation schema will generate 24 fragments of the fact table. • All cells of a fragmentation attribute have the same value: this means that it will not participate in the fragmentation process. Table 2.1 gives an example of a fragmentation schema, where all sub domains of Season (of dimension table Time) have the same value; consequently, it will not participate in fragmenting the warehouse schema. • Some cells has the same value: their corresponding sub domains will be merged into one. In Table 2.1, the first ([0, 18]) and the second (]18, 60[) sub domains of Age will be merged to form only one sub domain which is the union of the merged sub domains ([0, 60[). The final fragmentation attributes are: Gender and Age of dimension table Customer.

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Table 2.1 Example of a fragmentation schema Table Customer Customer Time

Attribute

SubDomain

SubDomain

SubDomain

Age Gender Season

1 1 1

1 2 1

2

SubDomain

1

1

4. Partition the fact table using referential partitioning based on the fragmentation schemes generated by our algorithm. To illustrate this step, let us consider an example. Suppose that our algorithm generates a fragmentation schema represented by a coding described by Table 2.1. Based on this coding, DBA can easily generate scripts using DDL to create a partitioned warehouse. This may be done as follows: Customer is partitioned using the composite mode (Range on attribute Age and List on attribute Gender) and the fact table using the native referential partitioning mode. CREATE TABLE Customer (CID NUMBER, Name Varchar2(20), Gender CHAR, Age Number) PARTITION BY RANGE (Age) SUBPARTITION BY LIST (Gender) SUBPARTITION TEMPLATE (SUBPARTITION Female VALUES (’F’), SUBPARTITION Male VALUES (’M’)) (PARTITION Cust_0_60 VALUES LESS THAN (61), PARTITION Cust_60_120 VALUES LESS THAN (MAXVALUE));

The fact table is also partitioned into 4 fragments as follows: CREATE TABLE Sales (customer_id NUMBER NOT NULL, product_id NUMBER NOT NULL, time_id Number NOT NULL, price NUMBER, quantity NUMBER, constraint Sales_customer_fk foreign key(customer_id) references CUSTOMER(CID)) PARTITION BY REFERENCE (Sales_customer_fk);

2.4 Experimental Results We have conducted an intensive experimental study to evaluate our proposed strategies for choosing dimension tables to referential partition a fact table. Since, the decision of choosing the horizontal partitioning technique is done before populating the data warehouse; the development of a mathematical model estimating the cost of processing a set of queries is required. In this study, we develop a cost model estimating the number of inputs outputs required for executing this set of queries. It takes into account size of generated fragments of the fact tables (kFi k; 1  i  N ); kTL , the number of occupied pages of each fragment Fi , denoted by jFi j D kFiPS where PS and T L represent respectively, the page size of disk and length of each

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L. Bellatreche 35000000 30000000 I/O cost

25000000

Prodlevel (Minimum share)

20000000 TimeLevel (High frequency)

15000000 10000000

Chanlevel (lowest table)

5000000 0 10

20 50 Threshold

100

Fig. 2.7 Theoretical results

instance of the fact table), buffer size, etc. The obtained results are then validated on Oracle11G DBMS in order to evaluate the effectiveness of our cost model. Dataset: We use the dataset from the APB1 benchmark [21]. The star schema of this benchmark has one fact table Actvars (33 323 400 tuples) and four dimension tables: Prodlevel (9,900 tuples), Custlevel (990 tuples), Timelevel (24 tuples) and Chanlevel (10 tuples). Workload: We have considered a workload of 55 single block queries with 55 selection predicates defined on 10 different attributes (Class Level, Group Level, Family Level, Line Level, Division Level, Year Level, Month Level, Quarter Level, Retailer Level, All Level). The domains of these attributes are split into: 4, 2, 5, 2, 4, 2, 12, 4, 4, 5 sub domains, respectively. We did not consider update queries (update and delete operations). Note that each selection predicate has a selectivity factor computed using SQL queries executed on the data set of APB1 benchmark. Our algorithms have been implemented using Visual CCC performed under Intel Pentium 4 with a memory of 3 Gb.

2.4.1 Theoretical Evaluation The used cost model computes the inputs and outputs required for executing the set of 38 queries1 . It takes into account the size of intermediate results of joins and the size of buffer. The cost final of 55 queries is computed as the sum of the cost of each query (for more details see [6]). We use a hill climbing algorithm to select fragmentation schema of the APB1 warehouse due to its simplicity and less computation time [3]. Other algorithms may

1

These queries are described in Appendix Section at: http://www.lisi.ensma.fr/ftp/pub/documents/ papers/2009/2009-DOLAP-Bellatreche.pdf

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be used such as simulated annealing and genetic [1]. Our hill climbing algorithm consists of the following two steps: 1. Find an initial solution and iteratively improve this solution by using hill climbing heuristics until no further reduction in total query processing cost. The initial solution is represented by multidimensional array, whose cells are filled in uniform way [3]. Each cell i of fragmentation attribute .Ak / is filled using the following formula:   i (2.3) ArrayŒi k D n where n is an integer (1  n  max1j C .nj /), where C is the number of fragmentation attributes. To illustrate this distribution, let us consider three fragmentation attributes: Gender, Season and Age, where the numbers of sub domains of each attributes are respectively, 2, 4 and 3 (see Fig. 2.8). The generated fragments of partitioning schema corresponding to n D 2; n D 3 and n D 4 are 12, 4 and 2 respectively. If DBA wants an initial fragmentation schema with a large number of fragments, she/he considers n with a low value. In this case, all initial sub domains (proposed by DBA) have the same probability to participate on the fragmentation process. 2. The previous initial solution can be improved by introducing two specialized operators, namely Merge and Split, which allow us to further reduce the total query processing cost due to evaluating queries in Q. Let us now focus on the formal definitions of these operators. Given a fragmentation attribute Ak of a dimension table Dj having F S.Dj / as fragmentation scheme, Merge operation takes as input two domain partitions of Ak 2 F S.Dj /, namely and returns as output a new fragmentation scheme for Dj , denoted by F S 0 .Dj /, where these two domains are merged into a singleton domain partition of Ak . Merge reduces the number of fragments generated by means of the fragmentation scheme

Gender

0

1

Season Age

0 0

1 1

1 2

2

1 1

1

1

n=2 Gender Season Age

0 0 0

0 0 0 n=3

Fig. 2.8 Different coding of an initial solution

Gender

0

0

Season

0

0

0

Age

0

0

0

n=4

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L. Bellatreche

F S.Dj / of dimension table Dj , hence it is used when the number of generated fragments does not satisfy the maintenance constraint W . Given a fragmentation attribute Ak of a dimension table Dj having F S.Dj / as fragmentation scheme, Split takes as input a domain partition of Ak in F S.Dj / and returns as output a new fragmentation scheme for Dj , denoted by F S.Dj /, where that domain is split into two distinct domain partitions. Split increases the number of fragments generated by means of the fragmentation scheme F S.Dj / of Dj . On the basis of these operators running on fragmentation schemes of dimensional tables, the hill climbing heuristic still finds the final solution, while the total query processing cost can be reduced and the maintenance constraint W can be satisfied. The first experiments evaluate the quality of each criterion: minimum share, high frequency, and largest size. The size and frequency of each candidate dimension to partition the fact table are easily obtained from the data set of APB1 benchmark and the 38 queries. The share criterion is more complicated to estimate, since it requires more advanced techniques such histograms [11]. For our case, we calculate the share using SQL queries executed on the data set of APB1 benchmark created on Oracle11G. For instance, the share of the dimension table CustLevel is computed by the following query: Select avg(Number) as AvgShare FROM (Select distinct customer_level, count(*) as Number From actvars Group By Customer_level);

For each criterion, we run our hill climbing algorithm with different values of the threshold (representing the number of generated fragments fixed by DBA) set to 10, 20, 50 and 100. For each obtained fragmentation schema, we estimate the cost of evaluating the 38 queries. At the end, 12 fragmentation schemes are obtained and evaluated. Figure 2.7 summarizes the obtained results. The main lessons behind these results are: (1) the minimum share criterion outperforms other criteria (frequency, maximum share), (2) minimum share and largest dimension table criteria give the same performance. This is because, in our real data warehouse, the table having the minimum share is the largest one and (3) the threshold has a great impact on query performance. Note that the fact of increasing the threshold does not mean getting a better performance, since when it is equal to 50 and 100; we got practically the same results. Having a large number of fragments increases the number of union operations which can be costly, especially when the size of manipulating partition instances is huge.

2.4.2 Validations on ORACLE 11G To validate the theoretical results, we conduct experiments using Oracle11G. We choose this DBMS because it supports referential horizontal partitioning. The data

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set of ABP1 benchmark is created and populated using generator programs offered by APB1 [21]. During this validation, we figure out that Composite partitioning with more than two modes is not directly supported by Oracle11G DDL2 . To deal with this problem, we propose the use of virtual partitioning column proposed by Oracle11G. A virtual column is an expression based on one or more existing columns in the table. The virtual column is only stored as meta-data and does not consume physical space. To illustrate the use of this column, let Di be a partitioned table in Ni fragments. Each instance of this table belongs to a particular fragment. The identification of the relevant fragment of a given instance is done by matching partitioning key values with instance values. To illustrate this mechanism, suppose that dimension table ProdLevel is partitioned into eight partitions by the hill climbing algorithm using three attributes. A virtual column PROD COL is added into ProdLevel in order to facilitate its partitioning using the List mode. mode: CREATE TABLE Prodlevel( CODE_LEVEL CHAR(12) NOT NULL, CLASS_LEVEL CHAR(12) NOT NULL, GROUP_LEVEL CHAR(12) NOT NULL, FAMILY_LEVEL CHAR(12) NOT NULL, LINE_LEVEL CHAR(12) NOT NULL, DIVISION_LEVEL CHAR(12) NOT NULL, PROD_COL NUMBER(5) generated always as (case when Class_Level IN(’ADX8MBFPWVIV’,’OC2WOOC8Q 6’,’LB2RKO0ZQCJD’) and Group_Level=’VTL9DOE3RSWQ’ and Family_Level in(’JIHR5NBAZWGU’,’OX3BXTCVRRKU’,’M32G5M3AC4T5’, ’Y45VKMTJDNYR’) then 0 when Class_Level IN (’ADX8MBFPWVIV’,’OC2WOOC8QIJ6’,’LB2RKO0ZQCJD’) and Group_Level=’VTL9DOE3RSWQ’ and Family_Level not in (’JIHR5NBAZWGU’,’OX3BXTCVRRKU’,’M32G5M3AC4T5’, ’Y45VKMTJDNYR’) then 1 when Class_Level IN (’ADX8MBFPWVIV’,’OC2WOOC8QIJ6’,’LB2RKO0ZQCJD’) and Group_Level not in (’VTL9DOE3RSWQ’) and Family_Level in (’JIHR5NBAZWGU’,’OX3BXTCVRRKU’,’M32G5M3AC4T5’,’Y45VKMTJDNYR’) then 2 when Class_Level IN (’ADX8MBFPWVIV’,’OC2WOOC8QIJ6’,’LB2RKO0ZQCJD’) and Group_Level not in (’VTL9DOE3RSWQ’) and Family_Level not in(’JIHR5NBAZWGU’,’OX3BXTCVRRKU’,’M32G5M3AC4T5’,’Y45VKMTJDNYR’) then 3 when Class_Level NOT IN (’ADX8MBFPWVIV’,’OC2WOOC8QIJ6’,’LB2RKO0ZQCJD’) and Group_Level=’VTL9DOE3RSWQ’ and Family_Level in (’JIHR5NBAZWGU’,’OX3BXTCVRRKU’,’M32G5M3AC4T5’,’Y45VKMTJDNYR’) then 4 when Class_Level NOT IN (’ADX8MBFPWVIV’,’OC2WOOC8QIJ6’,’LB2RKO0ZQCJD’) and Group_Level=’VTL9DOE3RSWQ’ and Family_Level not in (’JIHR5NBAZWGU’,’OX3BXTCVRRKU’,’M32G5M3AC4T5’,’Y45VKMTJDNYR’) then 5 Else 7 end), PRIMARY KEY (CODE_LEVEL)) PARTITION BY LIST(PROD_COL) (PARTITION PT1 VALUES(0) TABLESPACE HCTB,PARTITION PT2 VALUES(1) TABLESPACE HCTB, PARTITION PT3 VALUES(2) TABLESPACE HCTB, PARTITION PT4 VALUES(3) TABLESPACE HCTB, PARTITION PT5 VALUES(4) TABLESPACE HCTB, PARTITION PT6 VALUES(5) TABLESPACE HCTB, PARTITION PT7 VALUES(6) TABLESPACE HCTB, PARTITION PT8 VALUES(7) TABLESPACE HCTB);

No materialized views and advanced indexes are created, except indexes on primary and foreign keys. Figure 2.9 compares the native referential partitioning and user driven referential partitioning. In both cases, the fact table is partitioned into eight partitions based on ProdLevel table. In user driven referential partitioning, the ProdLevel is horizontally partitioned using the hash mode on the primary key (Code Level) and the fact table is horizontally partitioned using also the hash mode on the foreign key (the number of partitions using hash must be a power of 2). Both partitioning are compared to the non partitioning case. The result shows that native horizontal partitioning 2

For example, we can partition a table using three fragmentation attributes.

34

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3235

Execution time

3000 2500 2000

No fragmentation

2210 1557

1500 1000

Reference partitioning (Actvars – Prodlevel) Hash partitioning (Actvars – Prodlevel)

500 0

Fig. 2.9 Native and user driven referential partitioning comparison

Fig. 2.10 Individual evaluation of impact of criterion on query

outperforms largely the two other cases. The user driven referential partitioning is not well adapted to optimize star join queries, where the non partitioning mode outperforms it. Figure 2.10 compares the quality of solution generated by each criteria: Minimum Share, Highest frequency and Lowest table. These criteria are also compared with the non partitioned case. To conduct this experiment, we execute three times our climbing algorithm with a threshold equal to 10. Each query is executed individually on each generated fragmentation schema (one schema per selection criterion) and its execution time (given in second) is computed using Enterprise manager of Oracle 11G. Based on the obtained results, we propose to DBA the following recommendations: • To improve performance of queries containing only one join and selection predicates involving only one dimension table, DBA has to partition the fact table based on that dimension table; without worrying about its characteristic (minimum share, highest frequency, etc.). More clearly, queries 1, 2, 5 optimized

2 Dimension Table Selection Strategies to Referential Partition

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with the minimum share strategy and queries 21, 22 and 23 with high frequency, etc. This case is very limited in real data warehouse applications, where queries involve all dimension tables. • To speed up queries involving several dimension tables, but only one of them has selection predicates, DBA has to partition his/her fact table based on the dimension table involving selection predicates. • To accelerate queries involving several dimension tables and each one contains selection predicates (this scenario is most representative in the real life), DBA has to partition his/her fact table based on the dimension table having a minimum share. Query 34 of our query benchmark is an example of this scenario, where two dimension tables are used ProdLevel (with class level D ’LB2RKO0ZQCJD) and TimeLevel (with two predicates Quarter level D ‘Q2’ and Year level D ‘1996’). Another example is about the query 38 of our benchmark, where two dimension tables are used, and each one has only one selection predicate. In this case, TimeLevel strategy (representing high frequency and also the table having a second minimum share) outperforms ChanLevel strategy representing the lowest table. To complete our understanding, we use “Set autotrace on” utility to get execution plan of the query 34. First, we execute it on the non partitioned data warehouse and we generate its execution plan. We identify that Oracle optimizer starts by joining fact table with ProdLevel table (which has the minimum share criterion)3 . Secondly, we execute the same query on a partitioned data warehouse obtained by fragmenting the fact table based on ProdLevel. The query optimizer starts by joining a partition of fact table with one of ProdLevel table and then does

3383

3500 3000 2500 Execution time

2213 2009

2000

2153

1673 1500 1000 500 0

Fig. 2.11 Evaluation of all strategies for overall queries

3

The Oracle cost-based optimizer recognizes star queries.

No fragmentation ProdLevel (Minimum share) TimeLevel (High frequency) CustLevel partitioning ChanLevel (Lowest table)

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other operation. Based on these two execution plans, we can conclude that query optimizer uses the same criteria (minimum share) for ordering join. Referential partitioning gives a partial order of star join queries problem. Figure 2.11 summarizes the performance of each criterion for all queries.

2.5 Advisor Tool To facilitate the use of our partitioning strategies of a given relational data warehouse, we develop a tool, called, ParAdmin offering nice graphical interfaces to DBA. It supports primary and referential partitioning. It may be connected to any DBMS. For our study, we choose Oracle, since it supports referential partitioning. Based on a workload of queries Q and the maintenance constraint representing the number of final fragments, it offers DBA several choices to simulate the partitioning. If they are satisfied, they can generate all the scripts necessary to partition the data warehouse to DBA. It is developed with under Visual C++. ParAdmin consists of a set of five modules: (1) meta-base querying module, (2) managing queries module, (3) horizontal partitioning selection module (HPSM) and (4) horizontal partitioning module and (5) query rewriting module. The meta-base querying module is a very important module which allows the tool to work with any type of DBMS. From a type of DBMS, user name and password the module allows to connect to that account and to collect some information from the meta-base. These information concerns logical and physical levels of the data warehouse. Information of logical level includes tables and attributes in these tables. Information of physical level includes optimization techniques used and a set of statistics on tables and attributes of the data warehouse (number of tuples, cardinality, etc.) (Fig. 2.12). The managing queries module allows DBA to define the workload of queries (Q) used to partition the data warehouse. An DBA has two possibilities to edit her/his queries: manually or by importing them from external files. This module offers DBA the possibility to add, delete, and update queries. It integrates a parser in order to check the syntax of queries and to identify errors (based on the used tables and attributes by each query). Horizontal partitioning selection module (HPSM) selects partitioning schema of the data warehouse. It requires as inputs a schema of data warehouse, a workload and a threshold W . Using these data; HPSM selects a partitioning schema (PS) that minimizes the cost of the workload and that respect the maintenance constraint W . Horizontal partitioning module fragments physically the data warehouse using partitioning schema obtained from HPSM if the DBA is satisfied with the proposed solution. It partitions dimension table(s) and propagates their partitioning to fact table. Query rewriting module rewrites each global query on the generated fragmentation schema. It starts with identifying valid fragments for each query; rewrites the

2 Dimension Table Selection Strategies to Referential Partition

37

Administrator Visualization of Current State of DB

Workload

Database

Tables Description of tables Attributes Domains of attributes

DB Oracle

Queries/updates Query Frequency Selectivity Factors of Predicates

DBMS Parameters Buffer, Page Size, etc.

Horizontal Partitioning Attribute candidates Domaine decomposition

Preparation

Setting W

Selection strategies

Choice of Tables

Recommandations

Script Generation

Execution of scripts

HP Schema

Parameter Setting

Choice of attributes

Fig. 2.12 Steps of our tool

global query on each relevant fragment. Finally, it performs union of the obtained results. The main functionalities of ParAdmin are described in Fig. 2.12. They were identified incrementally by our Ph.D. students of our laboratory (LISI) following my advanced databases course. 1. The first functionality is to display the current state of the database (the schema, attributes, size of each table, definition of each attribute, etc.) and the workload (description of queries, number of selection operations, selection predicates, etc.). Figure 2.13 shows an example of visualisation proposed by our tool. 2. The second functionality consists in preparing the partitioning process, by offering different choices to select dimension table(s) (minimum share, largest table, most frequently table) to be used to partition the fact table, fragmentation attributes of the chosen table(s), number of final fragments, etc. (Fig. 2.14).

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Fig. 2.13 Visualization of current state of the database

Fig. 2.14 Choice of dimension table(s)

3. Once the preparation phase done, DBA may run the partitioning algorithm that proposes recommendations. DBA has the possibility to observe the cost of query before and after partitioning. As consequence, she/he can decide on considering or not the proposed partitioning. Her/his decision is based on the gain offered by horizontal partitioning for each query established using a threshold (Fig. 2.15).

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Fig. 2.15 Recommendations given to DBA

4. Finally, our tool proposes a functionality that generates scripts for primary and referential partitioning (if DBA is satisfied) corresponding to the target DBMS. They can be directly executed to really partition the warehouse.

2.6 Conclusion Horizontal data partitioning is one of important aspects of physical design of advanced database systems. Two horizontal partitioning types are identified: mono table partitioning and dependent-table partitioning. The first one mainly used in traditional databases to optimize selections. The second one is well adapted for business intelligence applications in order to optimize selections and joins defined in mega queries involving a large number of tables. We propose a comprehensive procedure for using referential partitioning. It first selects relevant dimension table(s) to partition the fact table and discards some; even they are associated with selection predicates. To perform this selection, we propose three strategies based on three factors: share, frequency and cardinality of each dimension table. The share criterion reduces the size of intermediate results of joins; therefore, it may be used in cascade over dimension tables. Two types of experimental studies were conducted to validate our findings: one using a mathematical cost model and another using Oracle11G with the APB1 benchmark. The obtained results are encouraging and recommendations are given to assist administrators on using the referential partitioning. Our procedure can be easily incorporated in any DBMS supporting referential partitioning such as Oracle11g. A tool, called ParAdmin is presented

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to assist DBAs to select the relevant dimension table(s) to partition the fact table. It gives recommendations to measure the quality of the fragmentation schemes proposed by our algorithm. An interesting issue needs to be addressed is to extend this work to the problem of selecting bitmap join indexes, where dimension table(s) are candidate to define each index.

References 1. Bellatreche, L., Boukhalfa, K., Abdalla, H.I.: SAGA: A Combination of Genetic and Simulated Annealing Algorithms for Physical Data Warehouse Design. In: Proceedings of BNCOD’06, pp. 212–219 (2006) 2. Bellatreche, L., Boukhalfa, K., Richard, P.: Data Partitioning in Data Warehouses: Hardness Study, Heuristics and ORACLE Validation. In: Proceedings of DaWaK’2008, pp. 87–96 (2008) 3. Bellatreche, L., Boukhalfa, K., Richard, P., Woameno, K.Y.: Referential Horizontal Partitioning Selection Problem in Data Warehouses: Hardness Study and Selection Algorithms. In IJDWM. 5(4), 1–23 (2009) 4. Bellatreche, L., Karlapalem, K., Simonet. A.: Algorithms and Support for Horizontal Class Partitioning in Object-Oriented Databases. In the Distributed and Parallel Databases Journal, 8(2), 155–179 (2000) 5. Bellatreche, L., Woameno, K.Y.: Dimension Table Driven Approach to Referential Partition Relational Data Warehouses. In: ACM 12th International Workshop on Data Warehousing and OLAP (DOLAP), pp. 9–16 (2009) 6. Boukhalfa, K.: De la Conception Physique aux Outils d’Administration et de Tuning des Entrepts de Donnes. Poitiers University, France, PhD. Thesis. (2009) 7. Ceri, S., Negri, M., Pelagatti, G.: Horizontal Data Partitioning in Database Design. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. SIGPLAN Notices, pp. 128–136 (1982) 8. Cho, W.S., Park, C.M., Whang, K.Y., So, S.H.: A New Method for estimating the number of objects satisfying an object-oriented query involving partial participation of classes. Inf. Syst. 21(3), 253–267 (1996) 9. Eadon, G., Chong, E.I., Shankar, S., Raghavan, A., Srinivasan, J., Das, S.: Supporting Table Partitioning By Reference in Oracle. In: Proceedings of SIGMOD’08, pp. 1111–1122 (2008) 10. Furtado, P.: Experimental evidence on partitioning in parallel data warehouses. In: Proceedings Of DOLAP, pp. 23–30 (2004) 11. Gibbons, P.B., Matias, Y., Poosala, V.: Fast incremental maintenance of approximate histograms. ACM Trans. Database Syst. 27(3), 261–298 (2002) 12. Gray, J., Slutz, D.: Data Mining the SDSS SkyServer Database. Microsoft Research, Technical Report MSR-TR-2002-01 (2002) 13. Karlapalem, K., Navathe, S.B., Ammar, M.: Optimal Redesign Policies to Support Dynamic Processing of Applications on a Distributed Database System. Information Systems, 21(4), 353–367 (1996) 14. Lei, H., Ross, K.A.: Faster Joins, Self-Joins and Multi-Way Joins Using Join Indices. In Data and Knowledge Engineering, 28(3), 277–298 (1998) 15. Legler, T., Lehner, W., Ross, A.: Query Optimization For Data Warehouse System With Different Data Distribution Strategies, In BTW, pp. 502–513 (2007) 16. Mahboubi, H., Darmont, J.: Data mining-based fragmentation of XML data warehouses. In: Proceedings DOLAP’08, pp. 9–16 (2008) 17. Munneke, D., Wahlstrom, K., Mohania, M.K.: Fragmentation of Multidimensional Databases. In: Proceedings of ADC’99 pp. 153–164 (1999)

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18. Neumann, T.: Query simplification: graceful degradation for join-order optimization. In: Proceedings of SIGMOD’09, pp. 403–414 (2009) 19. Noaman, A.Y., Barker, K.: A Horizontal Fragmentation Algorithm for the Fact Relation in a Distributed Data Warehouse. In: Proceedings of CIKM’99, pp. 154–161 (1999) 20. Oracle Data Sheet: Oracle Partitioning (2007) White Paper: http://www.oracle.com/ technology/products/bi/db/11g 21. OLAP Council: APB-1 OLAP Benchmark, Release II. http://www.olapcouncil.org/research/ bmarkly.htm (1998) ¨ 22. Ozsu, M.T., Valduriez, P.: Principles of Distributed Database Systems, Second Ed. Prentice Hall (1999) 23. Sanjay, A., Narasayya, V.R., Yang, B.: Integrating Vertical and Horizontal Partitioning Into Automated Physical Database Design. In: Proceedings of SIGMOD’04, pp. 359–370 (2004) 24. Simon, E.: Reality check: a case study of an EII research prototype encountering customer needs. In Proceedings of EDBT’08, pp. 1 (2008) 25. St¨ohr, T., M¨artens, H., Rahm, E.: Multi-Dimensional Database Allocation for Parallel Data Warehouses. In: Proceedings of VLDB2000, pp. 273–284 (2000) 26. Steinbrunn, M., Moerkotte, G., Kemper, A.: Heuristic and Randomized Optimization for the Join Ordering Problem. In VLDB Journal. 6(3), 191–208 (1997) 27. Swami, A.N., Schiefer, K.B.: On the Estimation of Join Result Sizes. In: Proceedings of EDBT’04, pp. 287–300 (1994) 28. Sybase: Sybase Adaptive Server Enterprise 15 Data Partitioning. White paper (2005)

•

Chapter 3

Observer-Based Adaptive Fuzzy Control of Nonlinear Time-Delay Systems Shabnam Pourdehi, Dena Karimipour, Navid Noroozi, and Faridoon Shabani

3.1 Introduction The control of uncertain nonlinear systems is of practical importance since many real world systems exhibit nonlinear dynamic behavior. To make the issue even more difficult, in many practical cases, the mathematical model is poorly known or uncertain. Therefore, in modeling and analysis of such systems, one needs to handle unknown nonlinearities and/or uncertain parameters. One of the most useful techniques applied to uncertain nonlinear systems are adaptive control methods which are able to compensate the lack of precise knowledge of the system [1, 7, 13, 26, 31]. Most adaptive control schemes designed for the nonlinear systems assume all the states to be available and their controllers and adaptive laws are consisting of information from the states [7, 30, 31]. But in many practical systems the state vector is not measurable, so it’s important to apply a state observer which is capable of estimating the unavailable states. The observer-based controllers utilize the estimation of the states instead of the states. The phenomenon of time delay is frequently a source of instability and exists in various engineering systems. Time delay usually leads to unsatisfactory performances. Therefore, the problem of stabilization of time delay systems has received considerable attention over the past years [5, 6, 33, 39]. To overcome this difficulty, the Lyapunov–Krasovskii functionals are used for stability analysis and synthesis [6, 33]. The variable structure control with sliding mode which was introduced to control by [32], is an effective tool to control nonlinear systems. Sliding mode controllers are not only known to be robust to model uncertainties and to parameter variations, but also they show good disturbance rejection properties. There has been a wide S. Pourdehi ()  D. Karimipour  N. Noroozi  F. Shabani School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran e-mail: Shabnamp [email protected] ¨ T. Ozyer et al. (eds.), Recent Trends in Information Reuse and Integration, DOI 10.1007/978-3-7091-0738-6 3, © Springer-Verlag/Wien 2012

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variety of applications of SMC in areas such as robotics [16,27], power control [40], aerospace [9, 24], and chaos control [15, 22, 43]. On the other hand, since the work of [17] was proposed in 1974, fuzzy control has been an active research topic. If there exist uncertainties in nonlinear terms, or the nonlinear terms are completely unknown, the fuzzy control algorithms can be useful. Based on the universal approximation theorem in fuzzy logic control, several stable adaptive fuzzy control schemes [13,26] have been developed to overcome the difficulty of extracting linguistic control rules from experts and to cope with the system parameter changes [1, 6, 13, 26, 39]. Combining adaptive fuzzy control and sliding mode approach provides robust control schemes for nonlinear systems with uncertainty [1, 6, 18]. The implementation of control inputs of practical systems is frequently subject to nonlinearity as a result of physical limitations, for example, saturation, backlash, dead-zone, and so on. The presence of nonlinearities in control input may cause serious degradation of system performance, a reduced rate of response. Therefore, it is clear that the effects of input nonlinearity must be taken into account in control design and realization of nonlinear systems [1, 26, 36, 39]. Chaos is a very interesting nonlinear phenomenon and has been studied extensively over the past years. It has the random-like behavior usually seen in stochastic systems although it is associated with deterministic dynamics. The control and synchronization of chaotic systems have received increased research attention, since the classical work on chaos control first presented by [20] and [21] introduced the pioneering work on the synchronization of chaotic systems. Synchronization of chaotic systems has been used in a variety of fields such as chemical reactions, biological systems and secure communities. In particular, chaos synchronization has been widely investigated for applications in secure communication [2,8,28,38]. The idea is that chaotic signal can be used as a carrier and transmitted together with an information signal to a receiver. Due to these applications, various control methods have been proposed to synchronize chaotic systems such as sliding mode design [15, 43], adaptive control [29, 34], fuzzy control [11, 14]. In this paper, it is shown that the proposed controller is applicable for the synchronization of two different chaotic systems. Most of the work cited before either consider a small group of nonlinear time-delayed systems or ignore the nonlinear effects in the control actuators. Furthermore, in many articles published on controlling time-delay systems, delay is assumed to be known. [33] proposed an adaptive fuzzy output tracking controller for a special class of perturbed time-delayed strict-feedback nonlinear systems, using the backstepping technique and Lyapunov–Krasovskii functionals. [6] has investigated the problem of variable structure adaptive fuzzy control for a class of nonlinear time-delay systems based on the Lyapunov–Krasovskii method. In both articles, the control inputs are assumed to be linear and the time-delay is known to the controller designer. In [39], a fuzzy sliding mode controller is employed to stabilize time-delayed systems subjected to input nonlinearities, where the system model is not a general class of nonlinear systems. To the best of our knowledge, the problem of control of a broad class of uncertain time-delayed nonlinear systems

3 Observer-Based Adaptive Fuzzy Control of Nonlinear Time-Delay Systems

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subjected to unknown delay, input nonlinearities and external disturbance has not been fully investigated yet. Motivated by the aforementioned reasons, the purpose of this paper lies in the development of adaptive controllers to control a class of nonlinear systems with unknown nonlinear terms, unknown time-delay and disturbances, when deadzone nonlinearity is present in the control input. An adaptive fuzzy sliding mode controller (SMC) and an adaptive observer-based controller are proposed to control this class of nonlinear system. Then, the Synchronization of two chaotic systems is studied which have the same form as the mentioned nonlinear system. The unknown nonlinear functions are approximated with fuzzy logic systems. The controllers have been designed to achieve the asymptotical stabilization even with the uncertainties and nonlinear input. These considerations make our design schemes more compatible with practical applications. The rest of the paper is divided into eight sections. In Sect. 3.2, some properties of the uncertain nonlinear time-delayed system with nonlinearities are reviewed. Section 3.3 presents a brief description of fuzzy logic systems. Section 3.4 describes the design approaches of the sliding surface and the reaching law. Section 3.5 presents the observer-based adaptive controller designed to stabilize the system. In Sect. 3.6, the synchronization of two time-delayed chaotic systems is given as an application. Simulation examples are given to demonstrate the effectiveness of method proposed in this paper in Sect. 3.7. Finally, the conclusion is given in Sect. 3.8.

3.2 Preliminaries A general description of uncertain time-delayed nonlinear dynamical systems is of the following form: xP D .A C A.t//x C .Ad C Ad .t//x.t  d/ C B..u/ C F.x; x.t  d/;  .t///; x.t/ D #.t/;

t 2 ŒdN 0

y D C x;

(3.1)

where x 2 Rn and u; y 2 R are states, control input and output of the nonlinear system, respectively. A 2 Rnn is the state matrix and Ad is the delay term matrix with appropriate dimension. A.t/ and Ad .t/ are time-varying uncertainties matrices of A and Ad . F.x; x.t  d /; .t// is an unknown function representing uncertainties containing the current state, delayed state and time-varying disturbance parameter .t /. '.u/ :R ! R is a continuous non-smooth function of u which represents the input nonlinearity. #.t/ is a continuous vector-valued initial function. d is the unknown delay parameter which assumed to be bounded by dN , 0  d  dN . The state of the system in (3.1) can be decomposed into two parts, x1 2 Rn1 ; x2 2 R and the matrix B is chosen as B D Œ0; : : : ; 0; 1T .

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Assumption 1. Uncertainties A.t/; Ad .t/ of the system in (3.1) satisfy the matching conditions A.t/ D BE1 .t/

kE1 .t/k  ı1

Ad .t/ D BE2 .t/

kE2 .t/k  ı2 ;

(3.2)

where ı1 and ı2 are unknown positive scalars. These conditions are so-called matching conditions. So kAk  1 ; kAd k  2 , where positive scalars 1 and 2 are not required to be known. Thus system in (3.1) is rewritten in the following form: xP 1 D A11 x1 C Ad11 x1 .t  d / C A12 x2 C Ad12 x2 .t  d /; x1 .t/ D #N 1 .t/;

t 2 ŒdN

0;

(3.3)

xP 2 D .A21 C A21 /x1 C .Ad 21 C Ad 21 .t//x1 .t  d / C .A22 C A22 /x2 C.Ad 22 C Ad 22 /x2 .t  d / C .u/ C F.x; x.t  d/; .t//; x2 .t/ D #N 2 .t/;

t 2 ŒdN

0;

(3.4)

where A11 ,A21 ,Ad11 ,Ad 21 .t/,. . . are the decomposed parts of A, Ad , A.t/ and Ad .t/ with appropriate dimensions. Then, the following assumptions are introduced. Assumption 2. F .x; x.t  d /; .t// satisfies the following decomposition: F .x; x.td; .t/// D F1 .x/CF2 .x.t  d //CF .x.t/; x.t  d /; .t//; (3.5) where F1 .x/ and F2 .x.t  d // are unknown continuous functions and F .x.t/; x.t  d /; .t// is an uncertain function which satisfies jF .x.t/; x.t  d /; .t//j  ˛0 C ˛1 kx.t/k C ˛2 kx.t  d /k ;

(3.6)

where ˛0 , ˛1 and ˛2 are unknown positive scalars. Assumption 3. The nonlinear input function '.u/ satisfies dead-zone nonlinearity which is described as [42] 8 < m.u  b1 / .u/ D 0 : m.u C b2 /

for u  b1 ; for  b2 < u < b1 ; for u  b2 ;

(3.7)

where m is a known positive scalar, but b 1 and b2 are unknown positive scalars.

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The function in (3.7) can be represented as .u/ D mu C w.u/ :

(3.8)

It can be seen that it is composed of a line with the slope m, together with a term w.u/. It is clear that w.u/ is bounded, and satisfies jw.u/j  w . Before proceeding with the main results of this paper, in the next section, some preliminaries are stated about the approximation ability of the fuzzy logic systems.

3.3 Function Approximation with Fuzzy Logic Systems The fuzzy system consists of the fuzzy rule base, fuzzy inference engine, fuzzifier and defuzzifier. A fuzzy rule base consists of the following fuzzy IF–THEN rules: j

R.j / W IF x1 is A1 & : : : &xn is Ajn ; THEN y i s B j :

(3.9)

The fuzzy logic systems with singleton fuzzifier, product inference engine, center-average defuzzifier are in the following form: Pl y.x/ D

j D1 y

Pl

j

j D1



Q n Qn

i D1 Aji .xi /

i D1

Aj .xi /

;

(3.10)

i

where x D Œx1 ; x2 ; : : : ; xn  2 Rn , Aj .xi / is the membership function of Aij , and i

y j represents a crisp value at which the membership function B j .y/ for output fuzzy set reaches its maximum. The universal fuzzy system with input vector x 2 ˝x for some compact set ˝x  Rn is proposed here to approximate the uncertain term y.x/ where  is a vector containing the tunable approximation parameters. From (3.10), the fuzzy system y.x/ can be expressed as y.x/ D  T .x/;

(3.11)

where  D .1 ; 2 ; : : : ; l /T , .x/ D . 1 .x/; 2 .x/; : : : ; l .x//T is a regressive vector with the regressor j .x/ defined as Qn

Aj .xi / i : Qn j D1 i D1 Aj .xi /

j .x/ D Pl

i D1

(3.12)

i

The above fuzzy logic system is capable of uniformly approximating any continuous nonlinear function over a compact set ˝x with any degree of accuracy. This property is shown by the following lemma.

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Lemma 1. For any given real continuous function f (x) on a compact set ˝x  Rn and a scalar " > 0, there exists a fuzzy logic system y.x/ in the form of (3.10) such that (3.13) sup jf .x/  y.x/j  ": x2˝x

In the next sections, the unknown nonlinear terms are replaced with their fuzzy approximation to any desired degree in controller synthesis for system in (3.1).

3.4 Proposed Adaptive Fuzzy Sliding Mode Controller This section proposes an adaptive variable structure control law to control the mentioned class of nonlinear systems using Lyapunov–Krasovskii stability theorem. Sliding mode control (SMC) is known to be an efficient control technique applicable to systems with profound nonlinearity and modeling uncertainty and it has attractive features such as fast response and good transient response. Theorem 1. Taking the sliding surface as follows S D CQ x D ŒC 1Œx1T x2 T ;

(3.14)

where C D "1 .PA12 /T , if assumption 1 is met and also the following inequality is satisfied: N

P .A11 C I / C.A11 C I /T P C "1 PD1 D1T P C "1 e 2 d PAd12 ATd12 P N

N

Ce 2 d PAd11 XATd11 P C X 1 C "1 e 2 d PD2 D2T P "1 .PA12 /  .PA12 /T < 0;

(3.15)

where P and X are positive matrices, " is a positive scalar and d is the time delay, then the sliding motion is exponentially stable depending on the bound of time-delay with a given decay rate  . In this paper we assume d to be unknown and just its upper bound is known. So, by some simple algebraic operations and using the definition of positive matrix, it can be shown that the inequality (3.15) is true even if replacing d with its upper bound dN . Proof. For system in (3.3), the following transformation is considered x1 .t/ D e  t z.t/:

(3.16)

We choose the Lyapunov–Krasovskii functional as Z V D zT P z C

t t d

"zT . /ŒX 1 C C T C z. /d :

(3.17)

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One can get VP D 2zT P .A11 C A12 C C I /z C 2e d zT P .Ad11 C Ad12 C /z.t  d / C "zT .t/ŒX 1 C C T C z.t/  "zT .t  d /ŒX 1 C C T C z.t  d /  2zT P .A11 C A12 C C I /z C "1 e 2d zT PAd12 ATd12 P z C "z.t  d /T C T C z.t  d / C "1 e 2d zT PAd11 XATd11 P z C "z.t  d /T X 1 z.t  d /C"zT X 1 zC"zT C T C z"z.t  d /T X 1 z.t  d /  "z.t  d /T C T C z.t  d /:

(3.18)

Letting C D "1 .PA12 /T , then VP D zT ŒP .A11 C I / C .A11 C I /T P z C "1 e 2d zT PAd12 ATd12 P z C "1 e 2d zT PAd11 XATd11 P z C "zT X 1 z  "1 zT PA12 AT12 P z: (3.19) If the inequality (3.15) is satisfied, then the derivative of the Lyapunov– Krasovskii functional is negative definite and theorem 1 is proved. t u In the rest of this section, the adaptive controller which can drive the trajectory of system in (3.1) onto the sliding surface is designed. For system in (3.1), consider the following controller: (3.20) u D u1 C u 2 ; where u1 D 

ˇ ˇ 1 sgn.CQ x/ ˇCQ x ˇ ; m

(3.21)

 1 1Cg kxk2 C e0 sgn.CQ x/ C e1 sgn.CQ x/ kxk m 2CQ x  2 ˇ ˇ 1 T T  ˇ ˇ  Q Q Q C sgn.C x/ C x C Ad C 1 1 .x/ C 2 2 .x/  ˇ CQ x; (3.22) 2

u2 D 

where ˇ is a positive scalar and , g, e0 , e1 , 1 and 2 are adaptive parameters, whose adaptive laws are as follows ˇ ˇ ˇ ˇ eP0 D k0 ˇCQ x ˇ ; eP1 D k1 ˇCQ x ˇ kxk ; P1 D 1 CQ x 1 .x/; P2 D 1 CQ x 1 .x/; gP D k2 kxk2 ; (3.23) where 1 and 2 are positive matrices with proper dimensions, k1 , k2 and k3 are positive scalars. In the above controller, u2 contains two fuzzy logic systems used to approximate the unknown nonlinear functions. Theorem 2. By using the control law in (3.20)–(3.22) and the adaptive laws in (3.23), the states of system in (3.1) converge to the previously defined sliding surface in (3.14).

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Proof. The candidate for the Lyapunov–Krasovskii functional for system in (3.1) is considered as follows: Z t Z 1 .ˇ 2 C 1/ t 1 2 S C V D jf2 .x. //j2 d C 2 kx. /k2 d 2m 2m t d 2m t d   1 N1 C N T 1 N2 C 1 k 1 gN 2 ; (3.24)  C k01 eN02 C k11 eN12 C N1T 1 1 2 2 2m 2 2 where eN0 D e0  e0 ; eN1 D e1  e1 ; N1 D 1  1 ; N2 D 2  2 ; gN D g   g. The time derivative along the state trajectories of the system in (3.1) is 1 .ˇ 2 C 1/ 1 VP D S SP C Œjf2 .x.t//j2  jf2 .x.t  d //j2  C 2 Œkx.t/k2 m 2m 2m  1 PN N T 1 PN  kx.t  d /k2  C k 1 eN0 ePN0 C k11 eN1 ePN1 C N1T 1 1  C 2 2 2 m 0  1 1 P C k2 gN gN : (3.25) 2 According to assumption 1, we get   CQ A    ; 1   CQ Ad     : 2

(3.26)

Based on (3.8) and (3.26), we have   

1 1 ˇˇ Q ˇˇ  C x CQ A kxk C CQ Ad  kx.t  d /k S SP D m m 1 C CQ xŒCQ Ax C CQ Ad x.t  d / C F1 .x.t// C F2 .x.t  d // m 1 ˇ ˇ 1 C ˇCQ x ˇ jF j C CQ x.u/ m m 1 ˇˇ Q ˇˇ  1 C x Œ1 kxk C 2 kx.t  d /k C CQ xŒCQ Ax C F1 .x.t//  m m   2 1 1 1 ˇˇ Q ˇˇ2  2   Q C x C Ad C kx.t  d /k C m 2 2   ˇ ˇ 1 1 ˇ Q ˇ2 1 2 C x C jF2 .x.t  d //j C m 2 2 1 1 ˇ ˇ (3.27) C ˇCQ x ˇ ŒjF j C jwj C CQ x.mu/: m m

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Using (3.6), one can obtain jF j C jwj  .˛0 C !  / C ˛1 kxk C ˛2 kx.t  d /k :

(3.28)

Substituting (3.26)–(3.28) into (3.25), one can obtain 1 VP .t/  CQ xŒ.˛0 C !  / C .˛1 C 1 / kxk C .˛2 C 2 / kx.t  d /k m 2 1 1 ˇ ˇ2  1 ˇˇ Q ˇˇ2 1 Cx C CQ xŒCQ AxCF1 .x.t//C ˇCQ x ˇ CQ Ad  C kx.td /k2 C m m 2m 2m 1 1 1 C jF2 .x.t  d //j2 C CQ xu C jF2 .x.t//j2  jF2 .x.t  d //j2 2m 2m 2m .ˇ  C 1/ .ˇ  C 1/ C 2 kxk2  2 kx.t  d /k2 2m 2m 1  1 P 1 1 P  PN N T 1 PN C N gN ; k0 eN0 eN0 C k11 eN1 ePN1 C N1T 1 1 1 C2 2 2 C k2 g m 2 2

2

(3.29)

ˇ0 ; ˇ1 and ˇ2 are considered as ˛0 C !  D ˇ0 ; ˛1 C 1 D ˇ1 ; ˛2 C 2 D ˇ1 :

(3.30)

The unknown nonlinear continuous functions are defined as the following CQ Ax C F1 .x.t// D FQ1 .x.t//; 1 jF2 .x.t//j2 D FQ2 .x.t//: 2CQ x

(3.31)

From the knowledge of Sect. 3.3, two fuzzy logic systems FO1 .xj1 / and FO2 .xj2 / can be used to approximate FQ1 .x.t// and FQ2 .x.t//. FO1 .xj1 / D 1T 1 .x/; FO2 .xj2 / D 2T 2 .x/:

(3.32)

According to lemma1, there exist optimal approximation parameters 1 and T T such that 1 1 .x/ and 2 2 .x/ can approximate FQ1 .x.t// and FQ2 .x.t// to any desired degree. We assume that the minimum approximation error satisfies the following assumption. 2 ,

T Assumption 4. The approximation error between FQ1 .x.t//, FQ2 .x.t//, 1 1 .x/ T and 2 2 .x/ satisfies the following inequality T T ŒFQ1 C FQ2  1 1 .x/  2 2 .x/  h0 C h1 kxk;

(3.33)

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where h0 and h1 are unknown scalars. These two parameters 1 and 2 will be learned through the adaptive algorithms in (3.23). Substituting controller in (3.21), (3.30)–(3.32) into the above inequality, we can obtain " ˇ ˇ  1 1 1 1 ˇˇ Q ˇˇ2  ˇCQ x ˇ Œˇ C ˇ kxk C CQ xŒFQ1 .x.t// C FQ2 .x.t// C VP .t/  Cx 0 1 m m m 2 # 2 2 1 ˇˇ ˇˇ2  ˇ2 1 ˇˇ Q ˇˇ2 C CQ x CQ Ad  C Cx kx.t  d /k2 C 2 2m 2m   ˇ ˇ 1 1 CCQ x  sgn.CQ x/ ˇCQ x ˇ C kx.t  d /k2 C CQ xu2 m 2m .ˇ22 C 1/ .ˇ 2 C 1/ kxk2  2 kx.t  d /k2 2m 2m   1 PN C N T 1 PN C 1 k 1 gN gPN :  C k01 eN0 ePN0 C k11 eN1 ePN1 C N1T 1 1 2 1 2 2 m 2 2 C

(3.34)

By doing some algebraic operations and replacing ˇ2 with g and using controller in (3.22), we get 2

1 VP .t/  CQ xŒFQ1 .x.t// C FQ2 .x.t//  1T 1 .x/  2T 2 .x/ m e0 ˇ ˇ 1 ˇ ˇ 1 C CQ xŒN1T 1 .x/ C N2T 2 .x/ C ˇCQ x ˇ Œˇ0 C ˇ1 kxk  ˇCQ x ˇ m m m ˇ ˇ .1 C g/ e1 1Cg  ˇCQ x ˇ kxk  kxk2 C kxk2  ˇ.CQ x/2 m 2m 2m   1 1 1 P 1 P 1 P 1 PN 1 PN N N C k eN0 eN0 C k1 eN1 eN1 C 1T 1 1 C 2T 2 2 C k2 gN gN : (3.35) m 0 2 Letting ˇ0 C h0 D e0 ; ˇ1 C h1 D e1 and using assumption 4, we have 1 ˇ ˇ 1 VP .t/  CQ xŒN1T 1 .x/ C N2T 2 .x/ C ˇCQ x ˇ Œ.e0  e0 / C .e1  e1 / kxk m m 1 1 C Œ.1 C g/  .1 C g/ kxk2  ˇ.CQ x/2 C .k01 eN0 ePN0 C k11 eN1 ePN1 2m m 1 1 P PN N T 1 PN C N1T 1 N g/: N (3.36) 1 1 C 2 2 2 C k2 g 2 Substituting adaptive laws in (3.23) into (3.36), we get the following inequality VP .t/  ˇ.CQ x/2 :

(3.37)

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According to the corollary of Barbalat’s lemma in Appendix 1, from (3.37) it is easy to see that the dynamics of system in (3.1) will enter into the desired sliding mode. So based on Lyapunov–Krasovskii stability theorem in Appendix 1, the asymptotical stability of the resulting closed-loop system is proved. t u Remark 1. A dead-zone input function is only a special case of nonlinear functions in this form. Therefore, it is straightforward to ensure that the developed control law of SMC can be applied to control the systems with other bounded nonlinearities in this form e.g. backlash hysteresis. Remark 2. In the control law in (3.20), we can use high slope saturation function sat. Sı / instead of signum function, sgn.S / where ı is a small positive constant. In addition, sgn.S / can be replaced by a fuzzy logic system in the same idea with [18] and [23]. Controlling large-scale systems in engineering applications, due to their widely applicability to many practical systems such as power systems and spacecrafts has become one of the most interesting topics [35, 37, 41]. The proposed controller in this section can be applied to large-scale systems. Consider a class of uncertain interconnected large-scale system with state delays composed of N subsystems: xP i D .Ai C Ai .t//xi C .Adi C Adi .t//xi .t  di / C Bi .i .ui / CFi .xi ; xi .t  di /;  i .t/// C

N X

Bj Hij .xj .t  ij /; t/ ;

i ¤j;j D1

xi .t/ D #i .t/; yi D Ci xi ;

t 2 ŒdNi 0 ;

for i D 1; : : : ; N :

for i D 1; : : : ; N

(3.38)

Hij are the interconnected matrices of the jth subsystem to the ith subsystem. di and ij are unknown fixed delays. Assumption 5. There exist some unknown positive constants &ij , such that   Hij .xj .t  ij /; t/  &ij

(3.39)

Corollary 1. For the uncertain system given in (3.38) which satisfies assumptions 1–5, if the control input is given by (3.20)–(3.22) with the adaptive laws by (3.23), then the closed-loop system is asymptotically stable. Proof. Satisfying assumption 5, the controller can be implemented without using any coupling state. It is really decentralized and the controller can be used separately for any subsystem. The proof is omitted. t u

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3.5 Observer Design In many practical systems, only partial information of the system states is available, so it is important to design a state observer and propose a control scheme which uses only the observed states. In this section, an adaptive observer is designed for system in (3.1), which utilizes only the observed states. Based on Lyapunov–Krasovskii stability theorem, stability of observed states and the states of system in (3.1) is proved. For system in (3.1) the following observer is considered O xPO D AxO C Bmu.t/ C B O1 .y  C x/;

(3.40)

xO 2 Rn is the vector of observed states and O1 is an adaptive parameter. Since the time-delay is not assumed to be known, the delayed states are not used in the dynamics of the proposed observer. Furthermore, the uncertainties, disturbances and unknown functions are not applied in the observer dynamics. To prove the stability of the system with observer the following assumptions are considered. Assumption 6. There exist matrix L and positive matrix P satisfying P .A  LC / C .A  LC /T P D Q1 ; BT P D C ;

(3.41)

where Q1 is also a positive definite matrix. Theorem 3. If assumption 1 and the following inequality are satisfied P Œ.A11 C I / C .A11 C I /T  "1 A12 AT12 P < 0

(3.42)

then by choosing the sliding surface as the following 



"1 T A12 P SD 2

1 xO D 0;

(3.43)

where P and X are positive matrices and " is a positive scalar, the sliding motion will be exponentially stable with a given decay rate  . To rewrite the sliding surface we have S D DQ xO D ŒD

1ŒxO 1

xO 2T T D D xO 1 C xO 2 D 0;

xPO 1 D A11 xO 1 C A12 D xO 1 :

(3.44) (3.45)

Proof. For system in (3.43), the following transformation is considered xO 1 D e  t zO:

(3.46)

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We choose the Lyapunov candidate as V D zOT P zO:

(3.47)

Taking the time derivative of V , one can get VP D 2OzT P zPO D 2OzT P .A11 C A12 D C I /Oz D zOT ŒP .A11 C I / C .A11 C I /T P Oz C 2OzT PA12 DOz:

(3.48)

1

If D D "2 AT12 P and the inequality (3.42) is satisfied, then the derivative of the Lyapunov function is negative definite and based on Lyapunov stability theorem in Appendix 1, theorem 3 is proved. t u In the rest of this section, we design a controller based on the observer in (3.40), which makes the closed loop system stable. For systems in (3.1) and (3.40), the following controller is considered uD

1 O O C O3T 3 .e/ C O4 /  ˇS ; .1 .y  C xO / C O2T 2 .x/ m

(3.49)

where ˇ is a positive scalar and 1 ; 4 2