Data Base Systems: Proceedings, 5th Informatik Symposium, IBM Germany, Bad Homburg v. d. H., September 24 - 26, 1975 (Lecture Notes in Computer Science)

Lecture Notes in Computer Science Edited by G. Goos and J. Hartmanis

39 Data Base Systems Proceedings, 5th informatik Symposium, IBM Germany, Bad Homburg v.d.H., September 24-26, 1975

Edited by H. Hasselmeier and W. G. Spruth

Springer-Verlag Berlin-Heidelberg • New York 19 76

Editorial Board P. Brinch H a n s e n . D. Gries o C. Moler • G. Seegm~iller. J. Stoer N. Wirth

Editors Helmut Hassetmeier Dr.-Ing. Wilhelm G. Spruth IBM D e u t s c h l a n d EF G r u n d l a g e n e n t w i c k l u n g S c h 6 n a i c h e r Stra6e 2 2 0 703 B0blingen/BRD

Library of Congress Cataloging in Publication Data

Informatik S~,~Do!~iL~a~ 5th~ }I~,~teg ,zo2 de." ~6he~ 19~'~. O&ta base system. (Lecture note~ .illeoa2%lter sciemce ; 39) Engl~ ~h o.r German. Sponsored by I~[~ G e ~ n y s~u& the I&~1 ~Torli T ~ e Co!~por atlono Bibliogr~p!~: p. Include-', i u ~ 1. Data base ~%nagement--Congresses. I. ~m,sse3~eia~ TI. I[o Spruth s W~ G. III. IBM De~Itschlan&o IV. IBM Wot'Id Trade Corporation. V. Title° VIo Series° QA76.9°D3152 19T~ 001.6'442 75-46~0 L

AMS Subject Classifications (1970): 00A10, 68-02, 68-03, 68A05, 68A10, 68A20, 6 8 A 5 0 CR Subject Classifications (1974): 4.30, 4.33, 4.34, 4.0, 4.22, 4.6

ISBN 3-540-07612-3 Springer-Verlag Berlin • Heidelberg • New York ISBN 0-387-07612-3 Springer-Verlag New Y o r k . Heidelberg • Berlin 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 machine or similar means, and. storage in data banks. Under § 54 of the German Copyright Law where copies are made for other than private use, a fee is payable to the publisher, the amount of the fee to be determined by agreement with the publisher. © by Springer-Verlag Berlin • Heidelberg 1976 Printed in Germany Printing and binding: Offsetdruckerei Julius Beltz, Hemsbach/Bergstr.

Contents Uberlegungen H.

Remus

zur E n t w i c k l u n g von D a t e n b a n k s y s t e m e n

.......................................................

On the R e l a t i o n s h i p b e t w e e n G.

Richter

D a t a Base Research: A.

I n f o r m a t i o n and Data 21

.....................................................

B~aser/H.

Schmutz

A Survey ...........................................

Grundlegendes

zur S p e i c h e r h i e r a r c h i e

C.

..................................................

Sch~nemann

44

114

S y s t e m R - A R e l a t i o n a l D a t a Base M a n a g e m e n t S y s t e m M.M.

Astrahan~

D.D.

Chamberlin,

W.F.

King,

I.L.

Traiger

........

139

G e o g r a p h i c Base Files: A p p l i c a t i o n s in the I n t e g r a t i o n and E x t r a c t i o n of D a t a f r o m D i v e r s e S o u r c e s P.E.

Mantey/E.D.

Carlson

.......................................

D a t a Base User L a n g u a g e s P.

Lockemann

for the N o n - P r o g r a m m e r

...................................................

Ein S y s t e m zur i n t e r a k t i v e n Messdaten U.

Schauer

149

183

Bearbeitung umfangreicher

.....................................................

213

D a t e n b a n k o r g a n i s a t i o n bei der H o e c h s t A k t i e n g e s e l l s c h a f t O.

Saal

........................................................

N u t z u n g von D a t e n b a n k e n einer H o c h s c h u l e E.

Edelhoff

im n i c h t - w i s s e n s c h a f t l i c h e n

R.

Heitm~ller

Clark

Data Base S y s t e m E v a l u a t i o n Hill ......................................................

H,L.

H.

Wedekind

Data Base S t a n d a r d i z a t i o n Steel

279

291

in D a t e n b a n k s y s t e m e n

....................................................

On the I n t e g r i t y of Data Bases and R e s o u r c e L o c k i n g R. B a y e r .......................................................

T.B.

266

Implementation

.....................................................

Datensicherheit

249

beim Hessischen

..................................................

Relational Data Dictionary I,A.

Bereich

....................................................

E i n s a t z eines D a t e n b a n k s y s t e m s Landeskriminalamt

232

315

339

- A Status R e p o r t

.....................................................

362

PREFACE

The papers in these Proceedings were presented at the 5th Informatik-Symposium which was held in Bad Homburg, Germany, from September 24 - 26, 1975. The Symposium was organized by the Scientific Relations Department of IBM Germany and sponsored by IBM Germany and the IBM World Trade Corporation.

The aim of the Informatik-Symposium is to strengthen and improve the com~unication between universities and industry, by covering a subject in the field of computer science, both from a university and from an industry point of view.

During the last 5-10 years, Data Base Systems have developed from a highly speculative "Management Information System (MIS)" approach to a practical production tool. In the late 5O's and early 60's, the application program was viewed as the nucleus of an application, with multiple data sets as accessories to the application program, and multiple, more or less unrelated application programs serving the needs of a larger enterprise or organization. The modern approach views the data base as the nucleus of a data processing operation, surrounded by multiple application programs operating on its data.

This switch has significantly increased the need for features and characteristics, which permit quick adaptions to an ever changing set of external requirements. In the old approach, external changes usually could be contained to one or a few application programs and their associated data sets. Because of the tight coupling between application programs and their data in a Data Base System, external changes are much more pervasive than they used to be. As a consequence, practical Data Base System implementations require a degree of universality and generality unknown in previous data processing installations.

In organizing this Symposium, we structured the subject matter into four topics~ The topic of data structures covers the logical view the user has on internally stored data. This topic is closely related to the subject of data base languages. In doing this, we specifically tried to avoid a repetition of the popular argumentation of the pros and cons of the various data representation models, e.g. the hierarchical, network, and relational models.

VL

The second topic deals with components and technology~ Today the magnetic disk is the main technology for the storage of large amounts of data. Its peculiarities impact to a large extent the structure of today's data base systems. A major change in data base structures can be expected, if and when we succeed to replace the magnetic disk storage by another, more amenable storage structure.

System aspects is the third topic° It includes problems of data security and data integrity. The evolution of data base systems has generated numerous ethical, social and moral questions. It is the responsibility of the data processing community to assure technically acceptable solutions for those issues°

User aspects is the fourth topic of the Symposium. Data Base Systems require a number of tools for their installation, maintenance, and evaluation. Refinement and enhancement of these tools may be one of the major prerequisites for the further development of Data Base Systems.

The editors would like to express their thanks to everybody who contributed to the Symposium by preparing a talk, providing advice for its content and organization or assisting in its administration~

Boeblingen, October 24, i975

H. Hasselmeier

W.G. Spruth

@berlegungen

zur Entwicklung

yon Datenbanksystemen

Horst Remus,

IBM Palo Alto, Californien

Zusammenfassung Bei der Entwicklung te besonders

zur integrierten

Datenverarbeitung

sind zwei Schrit-

bemerkenswert:

- Die Datenbank

als Zentrale,

wobei die Anwendungsprogramme

lichen den Verkehr mit der Datenbank

regeln

im wesent-

(Abfrage oder Aufarbei-

tung). - Das Datenfernverarbeitungsnetzwerk,

das den gleichzeitigen

Zugriff

einem Programm oder einer Datenbank yon mehreren Benutzerstationen

zu aus

gestattet. Die Datenbankzentrale

des Datenverarbeitungssystems

Datei als Zugriffsdatei

fur ein bestimmtes

der Datei yon diesem einen Programm) bezUglich

ihrer Organisation.

genereller

Datenbanksysteme

re @berlegungen Benutzer

-

Programm

Ein weiterer

Schritt

0berlegungen

ist die EinfUgung

mit der Idee der Datenunabh~ngigkeit. ("integrity"

zu der

(mit OPEN und CLOSE

erfordert bestimmte

haben mit der Beantwortungszeit

schutz und Datensicherung

im Gegensatz

("performance"),

und "recovery")

AndeDaten-

zu tun° FUr den

stellt sich das System in zwei Teilen dar:

Das Datenmodell

- Die Sprache mit der diese Daten manipuliert KUnftig

werden

("user interface").

zu 15sende Probleme weisen in die Richtung yon Datenbanken mit

gleichzeitigem schiedene

Zugriff von mehreren

Knotenpunkte

verteilte

Systemen und in Netzwerken

Datenbanken.

auf ver-

]~

ENTWICKLUNG ZUR DATENBANK

Wir betrachten Mengen~ deren Elemente aus alphanumerischen Zeichen zusammengesetzte Daten oder Informationen sind. F@r diese Mengen ergeben sich folgende Operationen: a) Die Abfrage~ d.h. die Herauskristallisierung

gewisser Teilinformation

aus der Gesamtmenge° b) Die Berichterstellung,

d.h. die (meist summarische)

der Informationsmenge,

Zusammenfassung

oder Teilen daraus, nach gewissen nicht not-

wendig automatisch in der Mengenstruktur gegebenen Merkmalen. c) Die Aufarbeit~ng der Informationsmenge,

d.h. HinzufSgung, Ausstreichen

oder Ver~ndern von Teilen der Informationsmenge.

(Eine spezielle Form

der Aufarbeitung ist die Format~nderung, d.h. das Hinzuf~gen oder Fortlassen yon Information relativ zu jeder vorhandenen Teilinformation.) Historisch gesehen ergibt sich bez@glich der Struktur oder Organisationsform yon Informationsmengen folgende Entwicklung

(Abbildung ] zeigt

einen Versuch zur schematischen Darstellung): Der erste Schritt zur Zusammenfassung yon Information ist die Liste, wobei die einfachste Form die fortlaufende Liste ist. Als Datentr~ger in der urspr@nglichen Form dienen Medien auf denen lesbar geschrieben werden konnte. Die Abfrage erfolgte manuell, die Liste wird nach dem infrage stehenden Eintrag

(normalerweise startend am Anfang der Liste)

durchsucht. Eine Berichterstellung

ist in den meisten Fallen unmSglich,

da Einzelabfragen sehr zeitraubend sindo Die Aufarbeitung erfolgt manuell durch Hinzuf~gung eines neuen Eintrags am Ende oder dutch Streichung ~berflSssig gewordener Eintr~ge. Eine ~nderung im Listenformat fiche Information per Eintrag) keiten, da die zus~tzliche

(zus~tz-

f@hrt normalerweise nicht zu Schwierig-

Information ohnehin nur f~r die neu hinzuge-

f@gten Eintr~ge verf~gbar ist. Der n~chste Schritt ist die geordnete Liste mit den gleichen Medien als Datentr~ger.

Eine geordnete Liste entsteht aus einer fortlaufenden Liste

durch Sortierung nach einem Ordnungsbegriff.

Es ist auch m~glich, dab

eine fortlaufende Liste automatisch geordnet ist, z.B. bei chronologischen Listen wie Kirchenbuchregistern.

Die Abfrage ist wesentlich vereinfacht und erleichtert damit die Berichterstellung.

Bei der Aufarbeitung treten Probleme mit der Einschiebung von

Eintr~gen auf. Jede Menge daf~r vorgesehener Platz ersch6pft sich. Das f@hrt entweder zu einer Zerst~rung der Ordnung oder es muss eine neue Liste erstellt werden. Ein gewisser Ausweg sind die Erg~nzungslisten und Hinweise auf solche in der Basisliste Gesamtinformation). @bersichtlichkeit,

(anstelle des Eintrags der

Derartige Verfahren f@hren jedoch schnell zur Unz.B. werden er6ffnungstheoretische

Werke f@r Schach

immer wieder neu aufgelegt. Der n~chste Schritt ware das Auseinanderbrechen der Liste in Einzeleintr~ge, die Kartei. Sie stellt gewisse spezielle Anspr~che an die Medien. Die Schwierigkeiten in der geordneten Liste bez@glich Hinzuf~gen von Eintr~gen si~d beseitigt. Die Erfindung der Lochkarte und die damit verbundene elektromechanische Behandlung von Information bedeutete die M6glichkeit, einzelne manuelle Verarbeitungsschritte

zu automatisieren. Die semi-automatisc~e Einzel-

abfrage ist jedoch im Normalfall zu zeitraubend. Die Berichterstellung kann weitgehend automatisch erfolgen, jedoch mu~ die Lochkartenkarte~ f~r das Programm, d.h. die Tabelliermaschinenschaltung, reitet werden

speziell vorbe-

(Sortieren, Mischen und andere spezielle Arbeitsg~nge).

Die Aufarbeitung erfolgt semi-automatisch.

Problematisch wird die For-

mat~nderung, die meist zur Erstellung einer neuen Kartei f~hrt. Benutzung anderer Medien wie Platte oder Band erm~glichen vollautomatische Verarbeitung und f@hren zur Datei. Normalerweise ist diese, ~hnlich wie die Lochkartenkartei, relativ zu einer bestimmten Anwendung organisiert. Der Programmierer "~ffnet"

(OPEN) und "schlie~t"

(CLOSE) die Datei,

je nachdem ob die zugeh6rige Anwendung l~uft oder nicht. L~uft die Anwendung nicht, wird die Datei unter Umst~nden sogar physikalisch vom System entfernt; jedenfalls ist sie normalerweise nicht f@r andere Anwendu~gen zugriffsbereit. Abfrage und Berichterstellung sind auch nur f~r bestimmte Anwendungsprogramme m6glich. Die gleichzeitige Bearbeitung mehrerer Anwendungen yon ein und derselben Datenstation oder yon einer oder mehr Anwendungen von verschiedenen Datenstationen wird problematisch. Aufarbeitung und Format~nderung erfordern die automatische Erstellung einer neuen Datei.

Eine Vielzahl

yon Anwendungen

menge f@hrt zur Datenbank.

und Benutzern

fNr ein und dieselbe Daten-

Ihre speziellen Erfordernisse

werden im fol-

genden n~her erl~utert.

2o

DATENBANKEN

Implizit

enthalten

minimalen

in der Definition

Redundanz

st~ndlichen Zugriff

UND DATENBANKSYSTEME

Struktur,

zu einer Datenbank

erfolgt normalerweise

@berwachung

ter. Neben der Erhaltung

Systemprogrammierer Beantwortungszeit physikalische

Anwendungen

der Datenbank

der Integrit~t

eine optimale und Speicher

Organisation

weise von Indizes

ist das Konzept der

einer f~r den Benutzer ver-

dem Datenmodell.

Benutzern mit verschiedenartigen eine fortlaufende

der Datenbank

und die Notwendigkeit

von einer Reihe yon

gleichzeitig.

durch einen Datenbankverwal-

der Datenbank

Erzielung

streben diese

von Leistungsfaktoren

an. Sie interessieren

der Datenbank~

Das erfordert

wie

sich daher f@r die

einschlie$1ich

der Wirkungs-

und Zeigern°

Die Anwendu~gsprogrammierer logische Datenmodell

oder "Enduser '~ interessieren

und f@r Wege zum Wiederauffinden

sich f~r das

und zur Aufarbei-

tung yon Datenbankelementen. Um zu verstehen~

welche Forderungen

der Anwendungsprogrammierer, wendungen Zun~chst

yon Datenbanken

oder Begriffs

yon Stapelverarbeitung

oder nachdem eine bestimmte Menge der Echtzeitverarbeitung tenmenge

(batch processing)

erinnert

erfolgt die Verarbeitung

gruppenweise

und

haben, m~ssen die An-

werden.

(real time processing)

Bei der Stapelverarbeitung Merkmales

n~her untersucht

sei an den Unterschied

und Echtzeitverarbeitung

beide, der Datenbankverwalter

an Datenbanksysteme

an bestimmten

zur Verarbeitung

(Abbildung

bez~glich

2),

eines

festgelegten angesammelt

Terminen ist. Bei

wird jeder Schritt sofort auf der gesamten Da-

ausgef~hrt.

Au~erdem sind bei den Anwendungen

zwei Parameter

von besonderer

tung: . die Voraussehbarkeit die H~ufigkeit gleichartiger

Zugriffe

(Repetivit~t).

Bedeu-

Hierbei gibt es bezNglich beider Merkmale eine Reihe yon Mischungen. Man wei~ z.B. nicht im voraus, nach welchem Tell eines Lagerbestands ein Magazinverwalter fragt. Was er darOber wissen will, ist jedoch genauestens bekannt.

Im allgemeinen kann man Datenbankoperationen

folgende verschiedenartige Operationen einteilen

in

(Abbildung 3):

I. Wirkungsvolle Ausffihrung sich wiederholender Arbeiten

(traditionelle

Stapelverarbeitung). 2. Im voraus definierte Abfragen 2

("Wie gro$ ist der Lagerbestand an

Zoll N~geln ?").

3. Zuf~llige, schlecht strukturierte und unvorhergesehene Abfragen

("Wie-

viele Ingenieure in Hamburg haben ein Monatseinkommen von mehr als DM 6000.-- ?"). Ein System, das Nr. I und 2 behandelt, wird "Operational"

oder "Supervisory System" genannt, ein System, das Nr. 3 behandelt, ein "Informa,ions" oder "Executive System". Beispiele for beide Gruppen w~ren: "Operational" Systeme: Bank mit Datenstationen an jedem Schalter, Flugreservierung, Flugsicherung. Informationssysteme;

BOcherei mit Aufsuchen von Information nach Kenn-

wort, Marktinformation fNr Management, Datenbank mit Personaldaten. Ein und dieselbe Datenbank sollte normalerweise die Anwendung beider Systeme erlauben.

3.

SPEZIELLE ANFORDERUNGEN AN DATENBANKEN

Es wurde bereits auf die Forderung der minimalen Redundanz hingewiesen. Die meisten Band-Bibliotheken enthalten eine FOlle von redundanten Daten. Unkontrollierte Behandlung der Frage der Redundanz kann (wie z.B. bei vielen BOroablagesystemen)

zu der Notwendigkeit h~ufiger Um- oder Neuord-

nung fOhren. Eine weitere Frage ist natOrlich der Verbrauch an Speicherplatz und die damit verbundene Kostenfrage. Mehrfache Kopien derselben Daten k6nnen au~erdem wegen eines m6glicherweise verschiedenen Aufarbeitungsstandes zu verschiedener Information fOhren. Ziel einer Datenbankorganisation sollte es also sein, Redundanz zu vermeiden, w o e s

6kono-

misch richtig

erscheint.

chen Wiederherstellung erforderlich

Aus Gr8nden der Datensicherheit

fehlerhafter

Daten kann jedoch einige Redundanz

sein.

Eine weitere Forderung

ist die Vielseitigkeit in der Darstellung von

Datenbeziehungen.

Verschiedene

logische

die jedoch alle auf derselben

Dateien,

Sehr bedeutend

Programmierer

Entscheidende

Benutzer

einer Datenstation

einheit,

die ein System bew~itigen

Verkehrsvolumen, (throughput)

Leistungsfaktoren

Bedeutung

erwarten

(Hinzuf@gen

Leistungssteigerung

der Obertragungen

tere Ma~nahmen in mehrere Datenbank

in Betracht

yon mehreren

beitungssysteme

in der Sekunde rasche

etc.).

ohne Bedeutung.

ist ein Dialog mit einer Antwortzeit

cheneinheit

yon Einflu$

Es ist notwendig,

Nat~rlich

and privacy"

Kontrollen

so gestaltet

nicht

zerst6rt werden

System mu~ daher die M6glich-

= Datenschutz).

gesch~tzt wer-

Diese Forderung

kann ~ber-

da~ das System die Authorisation

und seiner Aktionen ~berpr~ft sollten

der Re-

beinhalten.

tragen werden auf die Forderung~ Benutzers

von 2 Sekunden

untereinander

In vielen F~llen m~ssen Daten vor dem Zugriff Unbefugter ("security

F~r

des Datenbanksystems.

oder andere "Unf~lle"

( D a t e n s i c h e r h e i t ). Jedes

den

Stapelverar-

ist die Leistungsf~higkeit

da~ Daten und ihre Beziehungen

keit yon Datensicherheitstests

der Datenbank

(Stapelverarbeitung).

auf die Leistungsf~higkeit

durch Maschinenfehlverhalten

sind wei-

Ihr Entwurfskriterium

gewisse

erforderlich.

Um die erfor-

aus. F~r traditionelle

des "batch processing"

Anwendungen

zu

oder Zugriff zu einer

ist die Effektivit~t oder weniger

erfor-

Steigerung

wie z.B. Aufspaltung

(Dezentralisierung)

ist die Antwortzeit

Es gibt heute

in den Griff zu bekommen,

zu ziehen,

Rechenanlagen

je Zeiteinheit

und Gro~banken.

Bank-Zweigstellen

besser

Einzeldatenbanken

je Zeit-

ist. Systeme mit hohem Verkehrsvo-

ist eine weitere

von weiteren

fur die

der Obertragungen

die 10 und mehr Obertragungen

Bei derartigen Anwendungen

derliche

beruhen.

kann. Es gibt Systeme mit geringerem

lumen sind z.B. Flugreservierungssysteme bereits Anwendungen,

Datenbank

sind die Antwortzeit

und die Anzahl

bei denen die Anzahl

von geringer

benutzen unterschiedliche

der Leistungsf~higkeit eines Datenbank-

sind die Aspekte

systems.

dern.

und zur mOgli-

(z.B. durch ein Passwort).

sein, da~ geschickte

nicht ohne weiteres

umgehen

k6nnen.

und notiert werden,

soda~ falscher Gebrauch

Programmierer

Auch sollten die Aktionen nachtr~glich

eines Die sie

~be~acht

herausgefunden

werden kann. Ebenso ist es erforderlich,

da~ die Datenbank selbst lau-

fend @berpr~ft werden kann. Au~erdem tritt die Forderung auf, Anwendungsprogramme unabh~ngig yon der Datenorganisation und Zugriffstechnik zu schreiben (Datenunabh~ngigkeit). Z.B. bietet IMS [3] einen gewissen Grad yon Datenunabh~ngigkeit, indem neue Datensegmente an bestimmten Punkten der Hierarchie ohne Programm~nderung hinzugef@gt werden k~nnen, oder auch die L~nge eines Datensatzes oder die Aufteilung der Datenbank in Datengruppen ge~ndert werden kann.

4.

DATENBANKSTRUKTUREN

Die Funktion einer Datenbank ist das Abspeichern der Daten und der Beziehungen zwischen den Daten. Die logische Beschreibung einer Datenbank wird das Datenbankschema genannt. Ein Schema definiert also das Datenmodell fur den Anwender. Ein Subschema ist die Aufgliederung der Datenbank f~r ein spezielles Anwendungsprogramm. Abbildung 4 zeigt das Zusammenwirken der verschiedenen Teile innerhalb eines Datenbanksystems und insbesondere die Bedeutung der Begriffe Schema und Subschema. Abbildung 5 zeigt die Aufgliederung einer Datenbank zur Arbeitsplatzbeschaffung. Die Beziehungen zwischen den einzelnen Dateien sind klar ersichtlich. Die Arbeitgeberdatei gibt die Einzelheiten zu dem Feld "Arbeitgebernummer",

die Talentdatei die Einzelheiten zu dem Feld "Gefor-

dertes Talent" in der Arbeitsplatzliste. form f~r Datenbankstrukturen:

Hierbei zeigt sich eine Haupt-

die hierarchische Gliederung.

Die Dateien

"Arbeitgebernummer"

und "Talentgruppe" sind Untergliederungen der Datei

"Arbeitsplatzliste"

~Eltern-Kind-Beziehung).

Die M@glichkeit Beziehungen zwischen den einzelnen Datenfeldern in der Datenbankstruktur zum Ausdruck zu bringen, hat zu drei wesentlichen Datenbankorganisationsformen gef@hrt: ]. Die hierarchische Datenbankstruktur

(Abbildung 6). Hierbei hat der

hSchste Level einen und nut einen Knotenpunkt,

die "Wurzel des Baumes".

Jeder Knotenpunkt eines anderen Levels erh~it genau einen Knotenpunkt in dem n~chsth6heren Level zugeordnet.

Knuth

[4] definiert

sprechend

einen Baum oder eine hierarchische

Struktur

ent-

als "eine endliche Menge T von einem oder mehr Knotenpunk-

ten mit a. einem speziell

ausgezeichneten

Knotenpunkt,

der Wurzel

des Baumes

und b. m~O verbleibenden

disjunkten

(unverbundenen)

wobei jede dieser Teilmengen Teilbgume

genannto"

IMS [3] verwendet

die hierarchische

2. Falls ein Knotenpunkt Ebene zurNckgef@hrt

Netzwerk

~'

bezeichnet.

Die entstehende

zeigt einige einfache

Komplexere existieren.

entstehen,

nur ein spezieller

Netzwerkstruktur wenn mehrfache,

ohne Redundanz

den Datenbankelementen

Abbildung

NatNrlich

7

ist

Fall dersel-

ist ein Stammbaum. Level

und Redundanz

zurNckgef~hrt

werden.

k6nnen

Die Aus-

[I] fNhren zu einer Netzwerkstruktur. auszukommen

und die Beziehungen Kalk@l darstellen

data base" nach Codd

zwischen

zu k6nnen,

(siehe ausf~hrliche

Be-

in [2]).

Die Grundoperationen

zur Formung neuer Datens~tze

Die Sprache

aus sehr elegant,

doch haben sich Implementierungen

Leistungsf~higkeit mit Datensgtzen

erscheint

sind Vereinigung

und Durchschnitt.

vom mathematischen

bisher wenig durchgesetzt.

auf dem gleichen Level

keit des Datenmodells manipuliert

des Wortes Sprachbe-

nicht algorithmisch

von Mehrfachindizes

als algebraischen

f@hrt zu der "relational

"Netz-

den Elementen verschiedener

auf Baumstrukturen

der Codasylgruppe

3. Die Forderung

schreibung

zwischen

Unter EinfNhrung

verwendet.

yon Netzwerkstrukturen.

oder Baumstruktur

Netzwerkstrukturen arbeitungen

"plex structures"

Beispiele

Beziehungen

Gebrauchs

wird im angloamerikanischen

einer einfachen

Strukturen

bestimmbare

nicht mehr

Struktur wird als

Wegen des vielseitigen

reich hgufig die Bezeichnung eine hierarchische

einer h6heren

werden soll, kann die Beschreibung

in der Datenindustrie

ben. Ein Beispiel

Datenbankstruktur.

auf mehr als einen Knotenpunkt

durch einen Baum erfolgen. werkstruktur

Teilmengen T I ..... Tm,

ein Baum ist. Diese Teilmengen werden

und Einfachheit

werden k6nnen.

aus Gr~nden der

Die Vorteile yon Datei

gliedern

sich um Obersichtlich-

der Sprache mit denen Beziehungen

Darstellungen

Form k~nnen durch Verwendung

Standpunkt

in "relational

von Mehrfachindizes

data base"-

und Redundanz

auf

obige Formen der hierarchischen oder Netzwerkstrukturen

zur~ckge-

f~hrt werden. Im Zusammenhang mit Datenbankstrukturen wird h~ufig yon Listen und Ringen gesprochen (chains or lists, rings). Diese Strukturen beziehen sich jedoch auf die Art, in der Datens~tze innerhalb einer Datei untereinander verbunden sind. Sie beschreiben daher Techniken, wie logische Strukturen aus physikalischen erreicht werden, w~hrend die unter I-3 beschriebenen Strukturen spezielle Formen logischer Strukturen darstelfen. Ein entscheidendes Element f~r beide, die Listen- als auch die Ringstruktur,

sind die Zeiger (pointer),

die yon einem auf den folgenden

Datensatz weisen. Bei der Ringstruktur sind dabei normalerweise zweiseitige Zeiger gebr~uchlich.

5.

DATENBESCHREIBUNGSSPRACHEN

Eine Sprache, die die logische Datenstruktur beschreibt,

sollte die

folgenden Forderungen erf@llen: Die Gliederung in Datenmengen wie Dateien, S~tze, Segmente, Datenelemente, sollte klar beschreibbar sein. Jeder Typ einer solchen Mengeneinheit sollte spezifisch bezeichnet sein (z.B. sollten 2 verschiedene Satztypen verschiedene Bezeichnungen haben). Die Untergliederung einer bestimmten Datenmenge in bestimmte Untermengen sollte klar erkennbar sein (welche Datenelemente in einer bestimmten Datengruppierung enthalten sind etc.). Die Aufeinanderfolge mug spezifiziert und Wiederholungen sollten aufgezeigt sein. Die Sprache sollte ausdr~cken, welche Datenelemente als Indizes benutzt werden. Beziehungen zwischen Satztypen, Segmenttypen etc., die die Grundlage der Datenstruktur bilden, m@ssen spezifiziert und klar bezeichnet werden.

10 Nach J. Martin [5] ergeben sich je nach dem Gesichtspunkt des Benutzers verschiedene Level der Datenbeschreibungssprachen (Abbildung 8): I. Die Sprache ffir den Anwendungsprogrammierer, schema beschreibt in DL/I

(z.B. die Datendivision

(PSB = program specification

2. Die genere!le Beschreibung bankverwalter

des Schemas der Datenbank,

ion). Die COBOL Datendivision

3. Die physikalische losgel6st

block)). die vom Daten-

angewandt wird (z.B.: DL/I logical data base descript-

einem Schema zu beschreiben. werden.

description).

die das Datenbanksub-

in COBOL oder die PSBs

erlaubt z.B. nicht, die Beziehungen

Datenbeschreibung

Im Gegensatz

(z.B.: DL/I physical data base

zur logischen Datenbeschreibung,

ist yon Hardware- und Speicherfiberlegungen,

doch fur Leistungsoptimierung Auger DL/I ist wahrscheinlich

in

Sie kann daher bier nicht verwendet

die v@llig

sind diese je-

sehr interessant.

CODASYLs data description language DDL

die bekannteste Datenbankoeschreibungssprache.

6.

0BERLEGUNGEN

BEI DER HARDWARE

Es sind Datenbanken yon der Gr6~enordnung Bytes bekannt. denkbar,

yon mehr als 4 Milliarden

Das entspricht 40-50 Platteneinheiten

eine Platteneinheit

igngerer Zugriffszeit

IBM 3330. Es ist

durch eine gr6~ere Speichereinheit

zu unterst~tzen,

mit

ghnlich wie beim virtuellen Spei-

cherkonzept zwischen Kernspeicher und Platte. Die vor etwa einem Jahr angekfindigte IBM 3850 liefert z.B. 103 bis 104 mehr Speicherraum mit einer um den Faktor 102 verlgngerten Zugriffszeit. Der Benutzer sieht das System als ein einziges Plattensystem, ffir Leistungsf~higkeitsbetrachtungen sind die Hardware-Parameter jedoch von gr6~ter Bedeutung. Zum Beispiel bestehen strenge Abh~ngigkeiten zwischen Antwortzeit, Obertragungsrate und Direktspeichergr6~e, oder Speicherverf@gbarkeit in der niedrigsten Stufe der Speicherhierarchie.

Die Antwortzeit wgchst mit der

0bertragungsrate und f~llt mit mehr Direktspeicherverf~gbarkeit (weniger paging). Die Obertragungsrate kann mit mehr Direktspeicher gesteigert werden.

11 Andere Hardware-Parameter sind nat~rlich die Geschwindigkeit des Computers, der Aufbau und die Komponenten des Nachrichtennetzes.

7.

AUSBLICK

Die zus~tzlichen Anforderungen f~r Erweiterungen bestehender oder Entwicklung zuk~nftiger Datenbanksysteme gliedern sich um die folgenden Aspekte: a) Steigerung der Leistungsf~higkeit.

Wachstum der Datenbank und der

Anzahl der Datenbankbenutzer erfordern h6here 0bertragungsraten und k@rzere Antwortzeiten.

Die Antwort liegt in geeigneteren Datenbank-

organisationen und einer Minimisierung von Verwaltungsfunktionen. Gewisse Hilfsmittel der Hersteller erm6glichen gin "tuning" der Datenbank, dazu ergeben sich Anwender-beeinflu~te Verbesserungsm6glichkeiten.

Gewisse Verbesserungen sind dutch geeignetere Verwendung

yon Hardware erzielbar (multiprocessing oder ~hnliche Verfahren). b) Fortlaufende Operation.

Die Forderung einer 24-st~ndigen Zugriffs-

m6glichkeit zur Datenbank f~hrt zu gewissen Konsequenzen bei der Implementierung. Zun~chst wird bei Unterbrechung durch Fehlverhalten eine schnelle Wiederherstellung der Datenbank und kurzfristige Wiederaufnahme der Operationen notwendig. Das erfordert die F~hrung eines schnell zugriffsbereiten "Journals". AuBerdem sollte an den besten Techniken zur Fehlerverh~tung,

-auffindung und -korrektur gearbeitet werden.

Eine weitere Forderung ist, die Datenbank - bei gleichzeitiger Fortf~hrung des Routinebetriebs - zu reorganisieren.

Ein Dictionary

[7]

kann dabei als wesentliche Hilfe zum Management der Datenbanken dienen. c) Einfachheit der Installierung und Benutzung.

Die Parameter, die zur

optimalen Organisation einer Datenbank f@hren, sind sehr komplex. Systemhersteller helfen allgemein mit automatischen Organisationshilfen oder Hinweisen in der Dokumentation. Die Frage der Installierbarkeit ist weitgehend identisch mit der M6glichkeit, die physikalische Representation der Datenbank zu verstehen. Wiederum kann ein Dictionary

[7] n~tzlich sein.

!2 Einfachheit der Benutzung h[ngt wesenzlich mit der Beschaffenheit der Sprachen zur Datenmanipulierung

und -beschreibung und dem "inter-

face" zu den Programmierungssprachen Weitere Funktionen,

ab.

die zur Vereinfachung

der Benutzung f8hren~ haben

mit der automatischen Regelung des Informationsflusses zu tun. wesentlich ist hierbei die Handhabung der Kontrollinformation (Kontrollbl~cke)~ wie sie z.B. bei der standard network architecture Um die sp~tere Benutzung zu vereinfachen, geh6rige Systeme auf die M6glichkeit

erfolgto

m8ssen Datenbanken und zu-

zur sp~teren Ver[nderung bzw.

Erweiterung ausge!egt sein.

Literatur [!] CODASYL~

"1974 Status Report on Data Base Activities"

(Z] Date, C.J.~ "An Introduction Addison-Wesley,

to Database Systems".

Reading, Mass.

~3~ Information Management

Ig75

System, "System/Application

Design Guide"

IBM Form No. SH 20-9025 [4] ~nuth, D.E.~ "The Art of Computer Programming3 Algorithms".

Addison-Wesley,

Reading, Mass.,

Vol. I, Fundamental

1968

[5i ~artin, J.~ "Computer Data Base Organization", Prentice-Hall, Englewood Cliffs, N.J., 1975 [6] Senko, M.E.~ Altman, E.Bo, Astrahan, M.M and Fehder, P.L., "Data Structures

and Accessing

IB~ Systems Journal [7] Uhrowczik,

in Data-Base Systems".

12, 30-93 (1973)

P.P., "Data Dictionary/Directories".

I~4 Systems Journal 12, 332-350

(]973)

Medium, das menschfiches Schreiben und Lesen erlaubt.

Fortlaufende Liste

Lochkarte

Band, Platte

Lochkartenkartei

Datei

Abbildung 1

ENTWICKLUNG ZUR DATENBANK

Datenbank

Medium,separierbar je Eintrag

Kartei

Geordnete Liste

Datentr~ger

Datendarstellung

Semiautomatisch, die Kartei wird fLir das entsprechende Programm vorbereitet

Manuell

Manuell, bestimmt durch zeitraubende Einzelabfragen

Berichterstellung

Automatisch unbegrenzt

Auto matisch soweit Information vorhanden unbegrenzt

Automatisch, beAutomatisch, die Datei wird fLir das schr~inkt auf die zu dieser Datei geh6ren- entsprechende Programm vorbereitet de Anwendung

Manuell oder semiautomatisch (sehr zeitraubend)

Manuelt, unter Benutzung des Ordn ungsbegriffs

Manuelles Durchsuchen (generell: Start am Anfang)

Abfrage

Automatisch t unbegrenzt

Automatisch, mit h~iufiger Neuerstellung

Semiautomatisch

Manuell, unbegrenztes Hinzuf~Jgen m6glich

H&ufige Neuerstellung wegen Aussch6pfung des Platzes fiJr ZufiJgungen

Manuelt, ZufLigung neuer Eintr~ige am Ende

Aufarbeitung

Automatisch unbegrenzt

Erfordert normalerweise Neuerstellung der Datei

Erfordert normaler~eise Neuersteliung der Kartei

Kein Problem, neues Format bleibt auf neue Eintrage beschr~inkt.

Formatanderung

Co

14 STAPELVERARBEITUNG

~ " - " " l m ~

{ BATCHPROCESSING)

GEMEINSAME~ ~

i 125.s,,7o.2~ llp

GEMEINSAME ( 26,5. )

•

J + ( 25.s., ~3.01 ) V

t 29.5. )

y

! !

+ ECHTZEITVERARBEITUNG

(

REALTIMEPROCESSING)

T

I' r

ABBILDUNG 2

15

Operational Systeme

InformationsSysteme

Zugriff

geplant oder vorausprogrammiert

spontan, nicht vorausprogrammiert

Typische Beispiele

Bankschalter Ftugreservierung

Verkaufsanalyse, Personalinformation

Typische Benutzer

Bankschalterbeamte, Vorarbeiter, Unteres Management

lnformationsstab, Mittleres Management, Assistentendes h6heren Management

Normalzweck

Unterstiitzung von Routine Operationen

Unterstlitzung von Planung und dringenden InformationsbediJrfnissen

Antwortzeit

Sekunden

Minuten oder Stunden

Implementierer der Anwendung

Programmierer

Informationsspezialist

lmplementierungszeit

Wochen oder Monate

Stunden

Typische Sprachen

COBOL, FORTRAN, PL/I

IQF, GIS

MERKMALE FOR DATENBANKSYSTEME (nachJames Martin) Abbildung 3

I

DATENBANK SYSTEM

1

ABBILDUNG 4

WIRKUNGSWEISE EINES DATENBANKSYSTEMS

SYSTEM PUFFER

ARBEITSBEREtCH DES PROGRAMMS

ANWENDUNGS PROGRAMM A

17

NAME

ADRESSE

NAME

I

ADRESSE

VERFOGBARKEIT

I

i

ERFAHRUNG

ARBE1TSKLIMA

AUSBILDUNG

t

l-t DATEN

GEHALT

SOZIALE LEISTUNGEN

ABBILDUNG 5

AUFGLtEDERUNG EINER DATENBANK A R B E I T S P L A T Z B E S C H A F F U N G

TALENT GRUPPE

TALENT DATEI

ARBEITGEBER NUMMER

ARBEITGEBERDATEI

ARBEITSPLATZLISTE

I

ABBILDUNG 6

HIERARCHISCHE DATENBANKSTRUKTUR

/ \

WURZEL

jl

1

LEVEL 4

LEVEL 3

LEVEL 2

LEVEL

~o

~BBILDUNG

7

DATENBAN KNETZWERKSTRUKTUREN

411

20

ANWENDUNGSPROGRAMMIERER t

SUBSCHEMA

A

i tSUBSOHEMAI ,,

-..../_...scHEMA ./~ZU

GLOBALE ODER GENERELLE DATENBANKBESCHREtBUNG ( DATENBANKVERWALTER)

AUTOMATISCHE AUSF(JHRUNG DURCH DATENBANKSYSTEM

I

PHYSIKALISCHEBESCHREIBUNG

Oa DNUNG SUBSCHEMA

PHYSIKAL1SCHE J SPEICHERZUORDNUNG

I DATENBANKBESCHREIBUNG

LEVEL DER DATENBESCHREIBUNGEN

ABBtLDUNG 8

On the ~ e l a t i o n s h i R Gernot Richter, (G~D),

Sf.

between Information

Gesellschaft

fuer

and Data und

Mathematik

Datenverarbeitung

Augustin

Summary On

the

background

analyzed

of a general

which explicitly

represeniation.

Using a conceptual

to talk about information on

the

representation

In

the

of

with

a

data base management

For information

discussed.

have

been

characterized their

functional

realization.

of

This

gives

in

[ANSI]

recognized

under to

level present

motivation

to

in the field of

allows for the exchange

roles

work stations than

Years ago this kind of functional (Instanz)

consideration.

In

a

of messages

which units

these functional or

within the system rather

and applied in [ABN]

introduce

functional

Recently

as

offices influence each other by communicating been

the

differentiation

communicating

There the term office

units

The significant

and representation

some topics concerning

in the sense of [DIN]. identified

been introduced

of C. A. Petri.

some ideas

~ystems

only by their function

technical

has already

which has been designed manipulation,

a view has been proven to be very useful

consisting

(Funktionseinheiten)

its

systems.

systems

them

and

and data a definition is outlined.

for conceptual

I. A model view of information

considers

systems a view is

information

are presented.

for the information

are

plea

(IMC)

and their

these considerations

data base technology conclude

units

system

structures

For the concepts of format light

between

of information

role of type declarations is shown.

model of information

distinguishes

following

by

units

a suggestion

has been chosen fox the

information messages.

complementary

systems

So the need has

functional

between offices.

the

To this

unit which kind

of

22

functional

units

the

concept of interfaces concept of channel: communication only

term c h a n n e l

(Kanal)

was given in [ABN].

as used in [ANSI] has a direct relation An i n t e r f a c e

The

to

the

is a system of rules which govern the

via a c o n s i d e r e d channel.

by its function within the system

Also a channel is c h a r a c t e r i z e d serving

as

a

facility

where

messages can be posted and taken by the c o m m u n i c a t i n g offices.

This

yields

a model view of information systems

which provides

d e c o m p o s i t i o n into two d i s t i n c t classes of functional - offices channels

-

gained

some

discussion

by the processes they can perform

characterized

by the states they can assume.

publicity,

base management

since

the

and in the area of s t a n d a r d i z a t i o n

With the above model in mind

publication

of

of

two

we

want

offices

recently

[ANSI]

has

is under

(IFIP/TC-2 and I~G)

(ISO/TC 97/SC 5).

via

adequate minimum c o n f i g u r a t i o n to information

To

systems

both in the world of s c i e n t i f i c r e s e a r c h

communication

units:

characterized

This model view applied to data

for the

to

do

a

close

one channel.

examine

the

look

to

the

This seems to be an

interrelation

between

and data.

i l l u s t r a t e this c o n f i g u r a t i o n

where offices are depicted

we use the graphic notation of [PET],

by boxes and channels

by

circles

(in

the

cited paper only e l e m e n t a r y offices and c h a n n e l s are considered). yields fig. is

I.

In the adopted model c o m m u n i c a t i o n

done by exchanging

messages

This

between both offices

via the linking channel.

The arrows in

the above figure only i n d i c a t e the possibility of access and are functional

n o

units.

A further aspect is depicted in fig. only sense if both c o m m u n i c a t i n g

I:

The exchange of messages

offices have a

common

makes

background

of

understanding,

which allows them to interpret the messages found in the

channel.

assumption

The

useful auxiliary

such a "uniwerse of discourse" is a very

of

model for

between t e c h n i c a l f u n c t i o n a l

the

understanding

units.

of

communication

also

23

2. Model i n f o r m a t i o n and abstraction

So

far no reference has been made to a distinction between i n f o r m a t i o n

and data.

But words as "represent"

mapping between two things. there

are

two

abstraction,

and "interpret" indicate

mappings to be considered.

i.e.

a kind

of

It is the goal of this section to show that Both have the nature of an

omission of features not to be considered - hut they

start at different points.

One

kind

of abstraction starts with the so-called initial i n f o r m a t i o n

(Ausgangsinformation), knowledge

which is to

be

understood

or ideas a person has about something

anything else). intended

For a certain

purpose

pragmatic

as

the

whole

context,

i.e.

pursuing

part of it. The information about a person e.g.

is different

for a d m i n i s t r a t i v e purposes and for medical purposes;

information

about

a

technical

from what is needed for e n g i n e e r i n g purposes.

result of the abstraction process information

has

been

(~odellinformation).

yields

indicates,

the

"engineering

called

In

[STEEL] the above abstraction is called the which

the

process for teaching purposes will be So it

i n t e n d e d purpose which controls the abstraction process.

model

an

it might be that not the whole information is needed

but only the "relevant"

different

of

(of the real world or

model".

is

the

In [DURI] the

the

(respective)

similar c o n s i d e r a t i o n s "engineering

The

term

that we are still on the information

of

abstraction,'

model information

level.

In the present

context

we do not adopt any definition of information;

the concept is

used in

the

sense

of

knowledge

or

idea

(about

something).

Thus

i n f o r m a t i o n is viewed as being of mental nature.

It

is

obvious,

that

depending

on

the

respective intended purpose

various abstractions can be performed on the same initial information.

It

is

not

information

of

interest

"exists"

in

this

presentation,

or not - whatever that

whether

the

model

means. However we found the

approach very useful which assumes a level of model information

(as did

also other authors).

Model i n f o r m a t i o n cannot be communicated directly nature.

There must be a r e p r e s e n t a t i o n of it

handed

out

to

the addressee

(on a medium)

which can be

(or which can he stored for later use).

Such a r e p r e s e n t a t i o n is what usually is called between information

because of its mental

"data".

The distinction

and its r e p r e s e n t a t i o n is the background

all the following ideas have been developed.

on

which

24

Now it is possible to show the other a b s t r a c t i o n is

of

a g u i t e different

sense of data)

nature.

C o n s i d e r some messages

which by a g r e e m e n t

have the same meaning.

mentioned above,

between

the

messages

"semantics"

model

information.

There are

informa±ion.

and the process of

Such

rules a

mapping

for

mapping

the

to

the

"interpretation".

So we have an abstraction

pertinent

representational

There

is

one

model

If

several

they all have the

from various r e p r e s e n t a t i o n s

by

ignoring

the

respective

problem

which

might have been apparent

C o n s i d e r i n g the c o m m u n i c a t i o n

already in the

beween an

author

audience he has the need of r e p r e s e n t i n g model information,

he wants to write reference

about.

language

represented

and

is

the

For

this

purpose

beneficial,

in

interpretation

representation

whenever

a

kind

which

of

the

(graphical)

information

following

emphasis

is

laid

and which

can

of which is agreed upon.

g r a p h i c a l language will be p r e s e n t e d in canonical

of

is called

peculiarities.

above discussion. the

information

mapping

usually

messages are mapped onto the same model information, "same meaning".

As

e x c h a n g e of messages is assumed to have the goal

model information.

to

offices

What is "same meaning" in the present case? Any

pointed out,

to exchange

(here in the

communicating

message is c o n s i d e r e d to be a r e p r e s e n t a t i o n of model already

which

and on

be

Such a

used the

for model

i n f o r m a t i o n rather than on one of its possible representations.

3. O u t l i n e s of a c o n c e p t u a l

model of i n f o r m a t i o n

Before dealing with any problems of r e p r e s e n t a t i o n

the

model

What is an adeguate

information

itself

have to be identified.

view of model i n f o r m a t i o n

with respect to a p p l i c a t i o n s ?

brings

least in the past)

us

into

argumentation models"

a

about

(at

This

network,

of

question

very c o n t r o v e r s a l area of

the a d v a n t a g e s and d e f i c i e n c i e s of so-called

(hierarchic,

considerations

properties

relational,

...).

For

"data general

we can avoid this topic by adopting a view which covers

the various ,'data models".

This view has been outlined in [DUHI]

and is

r e f l e c t e d in a c o n c e p t u a l system called I n f o r m a t i o n M a n a g e m e n t C o n c e p t s (IMC).

These c o n c e p t s have been developed as a means for talking about

model information, systems.

in p a r t i c u l a r in the context

Simultaneously,

rules

for

graphic

i n f o r m a t i o n in terms of IMC were developed. IMC

r e p r e s e n t a t i o n of model

Both the basic concepts of

and the related c a n o n i c a l r e p r e s e n t a t i o n s

section to f a c i l i t a t e the treatment of the

of data base management

will be outlined in this

topic

of

"data"

(in

the

25

sense

In

of representation)

IMC

any portion

communication information library,

to

in a factory.

component

Depending

on

aggregate

is

A

way

either

of a

These

immediate

generic

unordered

a

(mathematical)

constructs.

The domain of a nomination

components

selection

of immediate

components

in the Vienna

To show examples

of atoms, above

the

vertex.

(fig.

always

nomina t i o n s

circles. network"

hy

example

of a "relation"

construct

is given

can

at

the

representation

of)

the same construct

the

nature

of

serve

e.g.

manner

[ZEM]).

framework

the

for the

(in the same

Beyond

of IMC.

we first

have

In IMC a box

is shown

either

In a tree r e p r e s e n t a t i o n is e x p r e s s e d

techniques the

is

by t ~

possible.

representation

by small circles are written

of

attached

close

to

the

and the c o r r e s p o n d i n g

of the nomination

we

we cannot.

For

"set

in [DKR].

may appear

representation

of model i n f o r m a t i o n

point

is a set of names.

cf.

In

The names

at the r e p r e s e n t a t i o n the same

boxes.

a to

aggregate

of names is depicted

representations.

whereas

representation.

3).

in that a

(Name)

of a c o n s t r u c t

an

an

nomination

n~me~

and n o m i n a t i o n s

(fig.

to

the

of both r e p r e s e n t a t i o n

in I~C r e p r e s e n t a t i o n

that

within

a

differ

Names only

Language,

canonical

represented

A detailed

If we look notice

a

be a

level)

constructs,

in a nomination

collections,

constructs

or

from

therefore

The c o m p o s i t i o n

the presence

to the component

of

or a n o m i n a t i o n

2) or by trees

of

A combination are

set

Definilion

mentioned

a construct.

aggregation

Atoms

is a in

cannot

(first

of aggregates

function

of names is involved

boxes

i.e.

as a part of

A construct

is of no significance.

to i n t r o d u c e

by nested

finite

of being a c o l l e c t i o n

immediate

the

a

(Atom)

to "be",

relevant

(Kollektion)

types

an

represents

an ~!Rm

its capacity

composition

collection

two

is

no meaning

in

itself.

is

that

in

may be the

an aggregate

i s

(Komponente).

to in a

a book

is either

which

construct

nomination

as s e l e c t o r s

A construct

situation),

of

collection

the property

can be referred

an atom is declared

(in a given

is a ~ R @ ~ 2 ~

the

(Nomination).

A construct

the c o m p o s i t i o n

communication.

within

which

(Gebilde).

Whereas

as e l e m e n t a r y

construct

to information.

a car in an administration,

(Aggregat).

construct,

considered

information

a construct

a family,

a process

be viewed

another

of model

is called

about

or an ~ e ~ a ~ e

compou n d

and its r e l a t i o n s h i p

various appears,

Therefore

of

fig.

in different

2

or

contexts.

locations

3

we In a

where

(the

on the c o n c e p t u a l

level

a concept

is

needed

which

26

allows

to

distinguish

between

different

appearances of one ccnstruct

(within a c o n s i d e r e d e m b r a c i n g construct). (Stelle) pairs

has

been introduced.

(name,

inserted

construct).

at

the

In IMC the concept of

In case of a c o l l e c t i o n the empty

name p o s i t i o n

in the pair.

in

(=relative to)

name

is

The first pair of a spot

d e f i n i n g s e q u e n c e always c o n s i s t s of the empty name and construct,

~R2~

A spot can be defined as a sequence of

the

which the spot is considered.

reference So with the

symbols of fig. ~ the c o n s t r u c t in question appears at the spots

(-,c,)

(home address,c2)

(-,ci)

(place of birth,c3)

(-,c,)

(branches, c s)

which are spots in cio construct.)

(city,c3)

(-,c~)

(The lower case c~s stand

The same c o n s t r u c t

for

the

respective

also appears at the spot

(-,c2)

(city,c~)

in c 2 and

(-,c5)

(-,c3)

in cs.

Another example is c 7 which appears in c, at the following two spots:

(-,c,)

(ho~e address~c 2)

(-,c,)

(date of birth,c 4)

It turns outs

(street,c6)

(number,cT)

(year,c,)

that the concept of spot is e s s e n t i a l

for the discussion

and u n d e r s t a n d i n g of some s o p h i s t i c a t e d

aspects in data base management

systems,

the

not least

information

Fig.

those

(constructs)

2 and 3 show,

always c o n s t r u c t s

concerning

and data

a t

system.

information

models

between

by the way, that in c a n o n i c a l graphic r e p r e s e n t a t i o n s p 0 t s

spo% structure is hierarchic, hierarchic

interrelationshi~

(representations).

But

it

are depicted.

one sigh% be is

obvious,

(in hierarchic,

network,

As by definition

tempted that

in

to

label

I~C

any a

a 1 1

existing

etc.)

the spots

relations,

form h i e r a r c h i c trees. So

far only individual c o n s t r u c t s

have been considered.

types or d e c l a r a t i o n s has been said nor used tacitly. is a set.

But not any set is a type.

determined

what are the e l e m e n t s

we focus on ~ e ~ _ ~ f constructs.

In

the

constructs world

First

of

of such a set. (Gebildetyp),

Nothing about

A type in general

all,

it

has

to

be

In the present context thus the

elements

are

of data base management systems instead of

27

"element"

the terms

"occurrence"

or "instance"

of

a

type

have

been

adopted. But

not

even

constructs

any

set

that only constructs for exchange. be

of constructs

has to be declared

the

specifies

"understood" are made,

via

is a construct type.

considered

channels

by interpretation.

should be called

type(s)

As only representations of

an

what constructs a "type

information

system,

can

a type

and

definition/declaration

is often called a "data definition

one

sloppy terminology

of

are admitted

can

be

in which type declarations

but unfortunately example

saying

of constructs

will be represented

A language,

A type of

communication,

which belong to the specified

Sore precisely:

exchanged

declaration

for a

language,,,

language".

This is

which is so characteristic

for the

field of data processing.

Not even "type declaration will

be

shown

below,

representational

level).

language', would be sufficiently

also

other types have to be declared

Therefore,

is a "construct type declaration composition

of

declaration, applied example

in

constructs

a graphic analogy

to

box

in

the to

representation, occurrence

the This

if

by others is specified

in a recursive type

the

type

definition

canonical

construct

of a particular type

in

particular.

the

be An

5, an occurrence

where in both figures the small

~[R@__~es~nation

emphasis

can

representation.

is shown in fig.

in fig. 6,

"type

language

plate"

is

a place for inserting (Typenbezeichnung)

also

used

in

as

the we

construct

is put on the fact that the construct

is

(cf. fig. 6 and 10).

It would be beyond the scope of this paper to discuss involved

(on the

such a language

As far as only the

upper righthand corner provides say.

speaking

As

(CTDL).

for a graphic type definition

name of the type or prefer

strictly

language"

construct

of that type is represented

precise.

the

aspects

concept of type in general and of construct

types in

The one or the other will he addressed

all in

the

following

paragraphs.

After this very short outline,

concepts to talk about model information

and a canonical

technique

representation

type has been emphasized

because

guestions of representation

of

are available.

its

to be discussed

great

The concept of

importance

for

in the next section.

the

28

~. Data as r e p r e s e n t a t i o n s For

convenience

the

term

" d i g i t a l data" i n d i c a t i n g which

consist

(pictures,

of

"data" that

characters

sounds,

etc.)

is used in the following instead of

only (cf.

are

not

representations

are

[DIN]).

representations

Other

investigated

considered

with regard to their

r e l a t i o n s h i p to information.

R e f e r r i n g to the c o n f i g u r a t i o n of two offices with (fig.

I),

let

the

piece

of

paper

on

r e a l i z a t i o n of a c o m m u n i c a t i o n channel. addressee

three,

that

one

agreement

seven",

or

A multitude

all r e p r e s e n t a t i o n s

there

might

of such

communication. irrelevant,

of

to

So

paper

in

text

taken the

carefully

as "number

for

shape

etc.

granted of

the

in

everyday

c h a r a c t e r s is

On the contrary,

between d i f f e r e n t fonts,

is

default in m a t h e m a t i c a l

literature.

beginning

Or:

In many of

in other places it is.

e x a m p l e s may show that the r e l a t i o n s h i p

and r e p r e s e n t a t i o n make possible

you

because they

meaning which usually is agreed upon at the

or

might

on the c o n s t r u c t level even

and a "plain seven"(7),

usual

~ + 3

and not be interpreted

a difference

are

according

languages the i n t e r s p e r s i o n of blanks in some places is

no relevance,

two

be

agreements

distinguish

programming

These

So

might be i n t e r p r e t e d as "number

but in m a t h e m a t i c a l texts it is not.

a different a

the

The example suggests the

between the c o m m u n i c a t i n g offices.

between a "bar seven"(~)

have

be a

whether

a c c o r d i n g to another agreement the r e p r e s e n t a t i o n

seven",

between

appears

The question is,

two or one construct.

be taken for an a r i t h m e t i c e x p r e s s i o n

have

channel

the i n t e r p r e t a t i o n of the various r e p r e s e n t a t i o n s is the

subject of a g r e e m e n t s to

a

fig. 7

i n t e r p r e t s the five r e p r e s e n t a t i o n s there as r e p r e s e n t a t i o n s

of five, four, answer,

which

(data)

has to be e s t a b l i s h e d

mutual u n d e r s t a n d i n g

between i n f o r m a t i o n

in advance in order to

in c o m m u n i c a t i o n

via a channel.

What

are the p r o v i s i o n s to be made? For a c o m m u n i c a t i o n background

of

to

be

possible

understanding,

r e p r e s e n t a t i o n s onto constructs. agreements

may

be

there

i.e.

a

must

be

a

prior

predefined

mapping

In the course of c o m m u n i c a t i o n

used to extend this cemmon background:

common of

further

One office

passes the d e c l a r a t i o n s to the other, the latter one accepts or rejects them.

The d e c l a r a t i o n s c o m p r i s e

29

- construct

type declaration

- representation

Construct The

type declaration.

type declarations

construct

communicated

were discussed

type declaration

via the considered

in

determines channel.

the

preceding

the constructs

The construct

type declaration

language is a part of the above mentioned common

background.

The representation

a

type.

It

constructs

what

are

type

we

arrive The

at

the

An example

may illustrate

representation intuitively.)

Fig.

to

of

ccnstruct of

channel.

occurrences

of

x~presentation

a

~

language

(RTDL)

mentioned common background.

the relationship

be

Although

necessary

indication

to

the

type declaration

type and their respective

are not

declared

representations

in the regarded

of

concept

representation

is a further part of the above

been

to

admissible

the set of all representations

(Darstellungtyp).

languages

refers the

of this type which can be exchanged

Considering given

type declaration

determines,

section.

which can be

discussed

between construct

occurrences.

here

and

should

it is a very simple example,

depict the ideas presented

type

and

(The used ad-hoc be

understood

many figures have

sc far,

which gives an

about the magnitude of usually implied declarations.

8 shows a declaration

MONTH-NAME,

of the four construct

YEAR and DAY-NUMBER.

types

CALENDAR-DATE,

The latter three are types of atoms,

the first one is an aggregate type.

Additionally

the type

composition

is shown in IMC representation.

Fig.

9

shows

MONTH REPR, the

a

pertaining

YEAR REPR,

declaration

construct types MONTH-NA~E,

DATE

PEPR

is

the

of four representation

and DAY REPR are the representation YEAR,

representation

and DAY-NUMBER, type

for

the

types:

types

for

respectively. construct

type

CALENDAR-DATE. In spite of the extensive remain:

The character sets to be used,

the medium

(paper e.g.)

to the pre-existing Fig. of

declarations

common

course

of

the

assumptions

the arrangement

and other details.

component

type DATE REPR.

of the construct types)

and

still

of characters

on

They all have %o be counted

background of the communicating

10 shows two occurrences

representation

many implicit

offices.

type CALENDAr-DATE

some

occurrences

of

(and the

30

This example suggests that the concept of format belongs to the concept of representation that

only

type.

one

type.

Up to here the assumption has been maintained,

representation

This restriction

of representation declared

type can be declared for each construct

should be dropped now.

If multiple declaration

types for one construct type is provided,

representation

types

close relation to the common use of this term. example of fig. could

9,

declare

representation

representation

of constructs of type

formats,

one "key-word"

It

be

and

can

explicit working

types

(=

above

type DATE HEPR we

formats)

CALENDAR-DATE

in

(two

for

the

"positional"

format).

observed that the separation of construct type declaration

representation in

declaration

(Format)

Referring to the

instead of the one representation

three

each of the

could be called a ~_m_a~

type

existing

decoration

systems.

The

is often simultaneously

area

format.

(=format layout

declaration)

of

the

construct

the specification of the input

This might be a reasonable economical

But to understand the relationship

is

between

information

and

not type and

approach. data

one

should be aware of the double function of such a "data definition". Applying

the

view which has been presented sc far of the relationship

between

information

(representation information

(constructs

and

and

representation

between

two

offices

construct

types)

we

types) outline

via one channel:

and a

flow

properties

(e.g. from a data base).

Office B finds the specified construct

representation

identifies the type of it,

of it),

of

the construct in question into the channel.

regarded channel,

type

(i.e. a

chooses one of the

type declarations and puts

conforms to the representation

of

An office B may be

requested by an office A to retrieve a construct with given

pertaining representation

data

a

representation

As this representation

declaration

established

office A is able to interpret the data

for

the

(knowing the

representation type and construct type). Some

reader

argumentation

might have noticed, is missing,

that in the CALENDAR-DATE example an

why the representations

details of the represented cons%lucts not necessarily processing,

so,

it

because

it

and not the construct. in a representation

only

(cf.

corresponds

fig. to

is %he representation

do not show all

10). Actually, the

practice

the

this is in

data

which occupies storage,

More extensive representations could be provided

type declaration

less extensive declarations,

for

various

etc.). Of course,

capacity of the involved channels

(storage).

reasons

(security,

that would require more In any case the question

31

arises,

whether such a "representation" is really a r e p r e s e n t a t i o n of a

construct.

Strictly speaking,

specifications,

it

r e p r e s e n t a t i o n is there.

Therefore

shows only the ~ a ~ i X ! ~ ! _ _ ~ construct,

is

not.

together

of

the

represented

in "input data")

This leads to the idea,

the

use

definition"

of

the

word

"data"

can partly be justified:

representation

type

in

the

be

entirely

clear

by

that

term ',data

The "data definition" defines

declaration

now,

With this in

criticized

the admissible data,

admissible individual parts of construct representations. should

that

usually means individual part of the full

r e p r e s e n t a t i o n rather than the full r e p r e s e n t a t i o n itself.

its

all

a full

because the r e p r e s e n t a t i o n a l part common to all occurrences

(e.g.

mind,

with

a r e p r e s e n t a t i o n in the a b o v e sense

(Individualteil)

of that type is in the type declarations. da~

Only

which allow the interpretation of the construct,

the

omission

in

i.e.

the

However,

it

of the word "type" is

misleading.

5. Practice oriented remarks

In this

concluding

section

some

applications

of

the

ideas

about

i n f o r m a t i o n and data as discussed above shall be tried.

First

a

preliminary remark:

system of IMC has been offered compete

with

other,

misunderstanding.

IMC

about information,

view

on

as a new proposal of a known

data

models.

data

That

that the model

would

%o

be

a

aiming to he a c o n c e p t u a l tool for speaking

on this level comprising the various

N e v e r t h e l e s s it is a specific

well is

There might be the impression,

c o n c e p % u a 1

data

models.

model and as such offers a

model information which allows to form a wariety of

i n f o r m a t i o n structures,

but has its own limitations,

too.

It is not the task of this paper to outline the features of hierarchic, network,

r e l a t i o n a l or other data models.

in

context,

this

so-called

to

Hut it might be of interest

what these attributes refer.

They refer %o %he

"data structures" which can be established

in a system of the

respective

model and which are supported by the

system's

functions.

With the t e r m i n o l o g y introduced above

we would of course say

" i n f o r m a t i o n structure', instead of "data structure" structure

in

representation efficiency, communication

our

understanding

as

structure

security,

or

purposes

the

any

goal

possible

else

of

structures

as meant here.

of

normally is left to the implementor,

manipulation

the

Data

information

in order to achieve this of

nature. constructs

For and

32

related q u e s t i o n s c o n c e r n i n g

model i n f o r m a t i o n are of main interest:

what levels of a g g r e g a t i o n are nominations what

are

the

restrictions

or

collections

for the nesting of constructs,

special generic types adjusted to the

application

in

On

available, are there

question

(e.g.

"relations",

which in terms of IMC are c o l l e c t i o n s of equally domained

nominations,

called c o l l e c t i v e s

orientation

in

extensive

address c o n s t r u c t s other

questions.

(Kollektiv)),

constructs,

what properties can be used to

(independently of their representation), The

answers

to

these

p e r t a i n i n g o p e r a t i o n s on the c o n s t r u c t s hierarchic,

It

is

a

network or r e l a t i o n a l

matter

of

course,

i n f l u e n c e d by r e p r e s e n t a t i o n of "redundancy" benefits

and

clarified, but

to

chance)

appearance

are of

are of relevance.

of

redundancy.

~ @ ! _ _ § ~

construct

"consistency

(cf.

constraints"

But

it

has

to be

constructs,

of

appears

an

embracing

(Parallelstelle).

type

that a

(necessarily or If

declaration

hy

the system it

will store the r e p r e s e n t a t i o n of the c o n s t r u c t each time it appears

(at

a

parallel

spot)

to be. that

or

It is c o n c e i v a b l e the

same

with the RESULT

(usually once).

The more often the

the higher the degree of redundancy is in p r i n c i p l e

technique

consistency-conditioned

the

less often

is stored,

decide,

Once a

whether

representation

is free to

this

so-called

the SOURCE clause of [DDLC]).

offices)

the

r e q u i r e d

s p e c i f i c a t i o n of this kind has been established,

(as one of the c o m m u n i c a t i n g

be

problem

It has been shown,

at several spots is

has to be specified in the

consistency

The

does not refer to the level of

construct

to

It is not intended here to consider the

at which the same c o n s t r u c t called

a

model

also e f f i c i e n c y and other aspects

techniques

disadvantages

Spots,

many

together with the

data

may appear at several spots as a component

construct. by

the

that r e d u n d a n c y

a

and

(or something else).

that

is one of them.

questions

render

the level of their representation.

construct

what is the support for

could

(and actually is done sometimes)

he

applied

p a r a l l e l spots.

feature of [DDLC]).

said

also

for

other

than

Such a s i t u a t i o n is also given

On the model i n f o r m a t i o n type level

RESULT clause specifies that the atom at the s p e c i f i e d spot is the

result of the e x e c u t i o n of a specified procedure, at other spots as input. additionally

is

In both the

specified,

SOURCE

which uses c o n s t r u c t s

and

the

RESULT

clause

whether a r e p r e s e n t a t i o n of the depending

atom is m a i n t a i n e d p e r m a n e n t l y

(ACTUAL)

by the system,

or is made up

only when r e q u i r e d for passing it via the c o m m u n i c a t i o n channel to r e q u e s t i n g office causes

redundancy.

(VIRTUAL). However

In the strict sense, also

i n t e r p r e t a t i o n of the ACTUAL and VIRTUAL

another,

the

the ACTUAL feature less

restrictive

feature is conceivable,

where

33

the

system still remains

assumed above)

free to follow the s p e c i f i c a t i o n

Doing a closer look to the d i s c u s s i o n of redundancy one encounters

a

(the "system")

is a

unit with a storage as a private channel fig.

11

is

configuration

often

preferred

containing

(input channel,

two

stated.

representations) RESULT

rather

than

are

the is

a

With

this

what is the object channel

which

the

As a matter of fact this is seldom clearly

input format declaration

(e.g.

sequence of atom

(e.g.

SOURCE feature,

made up to one complex declaration package,

d e c l a r a t i o n into the same package.

well known under

1.

a diagram

we have also three places to

complexity of which is still more increased by

"optimization"

fig.

and data base format declaration

feature)

functional

If we consider a r e p r e s e n t a t i o n tyFe declaration,

is applied to?

In particular,

To show explicitly

computerized

channels or still better three channels

the question has to be answered, declaration

configuration

(the "data base"),

data base, output channel)

represent constructs.

type

(in the context of

system

is a slight modification of that used so far.

that one of the offices

like

(as

or to understand it only as an efficiency constraint

data base management systems) which

verbatim

label

"schema',.

minimization

of

packing

the

construct

Such d e c l a r a t i o n packages The

consequence

of

the

are

such

an

the number of characters to be

written by the programmer at the expense of

quality

of

software,

in

particular of clarity.

Finally

some

remarks on the relationship between information

on %he one hand and their manipulation appropriate. or their

on

the

other

hand

and data might

be

If would be an obvious question to ask whether constructs

representations

are

r e p r e s e n t a t i o n s can he handled,

manipulated.

Strictly

speaking,

as was stated previously.

only

But so-called

data

manipulation languages do not refer to the r e p r e s e n t a t i o n a l level

only.

Primarily they are designed for the manipulation of constructs.

This will be illustrated by an example of the retrieval of a construct: The properties which are specified as parameters of a request refer a

construct

rather than to a r e p r e s e n t a t i o n of it.

to

The delivery of the

found construct is done by putting it into the respective channel in an agreed representation, is "navigation".

i.e.

meeting the output format.

This term refers to moving from one spot to the other

in an e x t e n s i v e construct.

Also here no reference to the r e p r e s e n t a t i o n

of this c o n s t r u c t is involved. some r e p r e s e n t a t i o n at.

Another example

Only upon request

of the construct

(at the spot)

In case of a data base management system,

the

navigator

gets

where he has arrived

he does not receive the

34 representation

on which the retrieval has been performed,

representation.

A counter-example,

representation in the data base in the output channel Although

a

information,

this

implementor,

does

the

user

has

reguirements. time

exert

language refers %o the level of model

not

imply

representations

accessed in order to execute several

representation

and

interests access.

to

way

application

of

adequacy

and

resources

will decrease. from

manipulation given

to

computer

computer

and

level.

a

efficiency.

concepts,

security

However,

of

update /

compromise

between

in overall

computing

information

differentiation

hand

computing

(traffic density, balanced

facilities to system interfaces,

view of inforaation

to

A good choice of

More and more it becomes evident,

includes to support conceptual presented

cost,

functions as well as a forecast

the involved people and the intended

to this goal.

On the other

the influence of storage and biased

access

it is up to the

which refer to storage and

should yield

considerations

actual

also the policies of

time,

acting in the future

etc.)

to

move

influence

has

some influence to the information

user's

no

B~t again,

in what way he has provided to be

He

These requirements

retrieval ratio, efficiency

that

manipulation commands.

construct types and of manipulation the

where the

is the same as

(librarian's counter).

takes place in the system.

which

is a library,

(room with book-shelves)

~'data manipulation"

representations

however,

but an output

time

that we have

stractures

and

where more preference is application. wherever

This goal

useful.

The

and data is intended to be a contribution

35

References

[DIN]

DIN/Fachnormenausschuss 44300 "Information Institute

[ANSI]

ANSI/X3/Sparc/DBMS

Study

GMD/Arbeitsgruppe the description (German).

[ PZT ]

Prozesse".

[DURI]

R. Durchholz

and

[DKR]

Beschreibung

Verlag,

"Concepts

T. B. Steel

"Data

Jr.,

IFIP-TC-2

"Abstract 10/5,

(German).

Datenbanksysteme,

E. Falkenberg

base

J. W. Klimbie

Conference

(German).

a "A

status

technical

1975 Elektronische

G. Richter, und

"Design of a data programs

(DAGS)"

Systementwuerfe

und W. Klutentreter,

fuer (Hrsg.),

1974

Description

CODASYL

data

197~

basic system for application Datenmodelle

Report".

for

Namur, January

Objects"

In:

CODASYL/Data

1967

1968

W. Klutentreter,

GMD, St. Augustin,

diskreter Haendler,

standardization

Special Working

Rechenanlagen R. Durchholz,

base

of the DDL",

H. Zemanek,

for

systems"

Basel,

Data Base Management,

(eds.), North-Holland,

base management

[ DDLC ]

zur

Birkhaeuser

In:

"Terminology computer

ueber Aufomatentheorie,

G. Richter,

systems".

American

1971

and K. L. Koffeman,

report".

Report.

fuer Betriebssystemnormung,

(Hrsg.),

DIN

German

1975

of models of job processing

in-depth evaluation [ZEM]

Interim

February

"Grundsaetzliches

Unger,

management

[STEEL]

Group,

In: 3. Colloguium

(~NI),

(German).

March 1972

Institute,

GMD, St. Augustin,

C. A. Petri, Peschl,

vocabulary"

for Standardization,

National Standards

lABS]

Informationsverarbeitung

processing;

Language Committee

DDL Journal of Development,

(DDLC), June 1973

"June 73

36

a~nd

I,,,office ..... %_______

Figure I

_

Configuration of con~unicating functional units

office

office

"user"

"system"

Figure 1!

office B

Extended configuration of communicating functional units

37

name

f•ly

home address

~

iJACKSON I

city

~

I HOUSTON1

~ ~

street

first name

FOHN BiJ

~

street name

place of birth

[ HOUSTON ]

[JAckSON

date of birth number

~

~

year ~ m o n t h

i~71 day branches

[WASHINGTON 1

LOS ANGELES]

[ANN A~oR, 1 t HO~-'STON ]

Figure 2

Constructsin iMC box representation

_

~

Figure 3

..........

.o~)sTo,.

~ % jhumber ~

~)street

i) home address .....

t

.....................

"

- -

/

~ /

X

. ~

/-~hvear f ~onth ~ d a y ~ y q ] ~ j ~ ~__

~"'~lace 7f~irth

.-......] 1 F~os ,,.,~,s]--

fir.~ame Sz~,e

"[ ..................

1 | / ~ branches

t

I ranmalmly~ name~

Constructsin IMC tree representation

streetf-~ name ~ ] -__ "~

k~ic i t y

f ~ ~

¢O O0

39

, ,, /C?. ~

name

f•iy

homeaddress

/

FJAc~SO~

city

~

C3

C~

first name

[JO~N '~-I C6 _

0 s<eeti

_

~

place of birth//

c3

1H o u s T o ~ ] ~

~c~

date of birth f

\ C7

1~7 day branches¢ ..~.,. IWASHINGTON ]

[~os A~G~Es I

j~.,~

[CAMBRIDGE

[ANN A~BOR ]

I~{ousTON

C3

Figure 4

Construct representation of fig. 2 with additional lettering for reference purposes

--c 5

40 EMPLOYEE

----

~ Figure 5

~DSCR

SKILLS

MBE R

Jt

Graphic construct type definition

EMPlOYeE

PERSON

¢

SKILLS~

....

IsKILLCODE I 1120 . J. WA=TERS ]

I

,ISK~LLCODE 1135

NUMBER

5 7 8 ~ Figure 6

Occurrence of construct type defined in fig. 5

41

Figure

see n e x t page

7

construct atom:

JANUARY,

construct atom:

FEBRUARY,

... D E C E M B E R

type Y E A R

1900~INTEGE~

construct atom:

type M O N T H - N A M E

1999

type D A Y - N U M B E R

1~INTEGER~31

construct

type C A L E N D A R - D A T E

nomination:

MONTH

--> c o n s t r u c t

type M O N T H - N A M E

YEAR

--> c o n s t r u c t

type Y E A R

DAY

--> c o n s t r u c t

type D A Y - N U M B E R

non-occurrences:

MONTH

DAY

FEBRUARY

3O

FEBRUARY

31

APRIL

31

etc. CALENDAR-DATE

,•MONTH

YEAR

0

atom

~A_Y-NUMBE__R om....

Figure

8

Construct

type d e c l a r a t i o n s

42

representation

type M O N T H REPR

r e p r e s e n t e d c o n s t r u c t type M O N T H - N A M Z string:

1

or

JAN --> a t o m J A N U A R Y

12

or

DEC --> a t o m D E C E M B E R

r e p r e s e n t a t i o n type DAY R E P R r e p r e s e n t e d c o n s t r u c t type D A Y - N U M B E R string:

DECIMAL representation

representation

type Y E A R R E P R

r e p r e s e n t e d c o n s t r u c t type Y E A R string:

DECIMAL representation

representation

type DATE R E P R

r e p r e s e n t e d c o n s t r u c t type C A L E N D A R - D A T E string: (DAY R E P R "-" M O N T H R E P R "-" Y E A R REPR) or (YEAR R E P R "-" M O N T H R E P R "-" DAY REPR) or ("D:" DAY R E P R /// "M:" M O N T H R E P R /// "Y:" Y E A R R E P R

Figure

9

Representation

; delimiter

",")

type d e c l a r a t i o n s

4+3 SEVEN seven

Figure 7

Five c o n s t r u c t r e p r e s e n t a t i o n s

on p a p e r

43

I'CALENDAR-DATE

DAY0 YEA~0 l DAY-N~M~'4 ' ] 19G7'YEAR1 MONTH 0

4-0CT-1967 D:4,Y: 1967,M:OCT

1967-10-4

I CALENDAR-DATE _ ~ MONTH

DAY_~

--1973 ]

< M:MAY,Y: 1973,D: 14

D:14,M:5,Y:1973 14-5-1973 1973-MAY-14

Figure 10

Construct type occurrences and representation type occurrences of fig. 8 and 9

Figure 11

see first page (fig. I)

Data

A®

Base

Eesearch:

Blase~

H.

A

Surve Z

Schm~%z~

Tiergartenst~.

IBM

Wissenschaftliches

Zentrum~

Heidelberg~

15

Abstract The

research

Most

of

models

activities

the of

issues

information~

implementation industry of

ac%ivl%ies

respect

area

of

tial

future

%0

da%~

OF

and

between

with

%rends

Introduction Models

3.

Data

Manlpulation

4.

System

data

modelling user

and and

and

data

data

systems

are

institutes

reviewed.

center

around

manip~lation~

system

and

Comparison

analysis.

requirements

development.

potentially

architecture

base

base

research

shows

emerging

are

principles

with

with

differences

Conclusions

and

aspects~

respect

drawn in

to

the

poten-

research°

Languages

Problems

~.

Storage

6.

Modelling

7.

Summary

8®

Bibliography

Structures and and

objective

and

Search

Algorithms

Analysis

Conclusions

INTRODUCTION

and

in

of

CONTENTS

Data

past

by

documented

research

des~n

I®

The

area

and

established

base

2.

1.

%he

interactive

±echniques

emphasis

TABLE

in

considered

of

present

192/

/49,

this

paper

research

is

primarily

activities

in

to

the

provide

data

an

base

overview

area.

This

over

~a--

45

per

does

not

er~

information

information

survey

retrieval

systems

of

such

an

introductlon

to

available

Ll~htfoot:

Jardlne

and

of

T

data

still

help

is a

or

have

been

such

a

The

the

scheme

an

first

our

shown

programs

is

seen

by

base

the

We

will

~

is

is

which

sical

or

internal

is

actually

we

can

The

use

are

the

of

between selec±

the

conceptual

conceptual

a~e

specified

in

the

in

conceptual the

never

in

subpart

a

and of

the

definition

external information

the

It

with

the

It

is

a

standard-shown

designer

IMS

in

through the

views

exist

of

a

serves

in

as

the

the

double

phy-

form~

help

of

All

of

The

these and

mappln~s

purpose:

sufficient

a

C[!),

administrator

language.

a

central the

Informatlon

[ mapping

mappings.

and

a

conceptual

with

the and

langua@e.

examplel

way

base

as a

at

syntax

base

of

to

installatlonT may

For

way

aspects

referred

"correct"

data

serve

the

or

mapping

neces~ry

informa--

legal

form

of

of

system

system

{fig.

is

information,

pepresents

the

the

data

mapping

and

of

usually

It

physical

internal

information

Is

reflects

Given

responsibility data

been

the

retrieval

information.

corresponding to

the

unconscioesly,

information

directly.

of

information

memory.

of

what

defining

used

vlews

of

type

grammar

is other

view

stored

construct

mapplng~

mappings

for

to

has

major

views

for is

~iven

a

describesv

view

(D.Ao

schemes

or

point.

group

For

specifies

point

a

to

during

responsible This

A

conceptu~l

Experlence

which

knowledge~

persons~

flow

level.

reference

by

similar

central

schema

The

J.A.

S[stems

accepted.

consciously

shows

administrator".

a

and

definition

widely

employed

very

data

group

to

in

commer-

addition

in

our

make

authors

the

information.

similar

Users

question,

is

scheme

conceptual

therefore

a

experience

conceptual is

the

Barnett

-- A

of

danger

interested

of

~{anagemen[ 1974}

already

I and

schema

{A,J.

the

ago.

and

view

IMS

Base

implemented~

fig.

as

of

iS

some

[IMS)

D~ta

the

decade

in

who

depth

Vurth--

aspects

aware

reader, In

Is

and To

who a

integrated

"data

debates.

book.

well

Amsterdam,

This

in

are

System

architecture

concept~l

information

the

of

nearly

is

The

stored,

system?

software,

survey.

(ANSI/X3/SPARC}

mappln~s~

tion.

this

~ which of

such

[n

base

non-compute~orlented

the

systems~

~olland~ in

WedekindVs

scheme

data

base

Management

subject

scheme

group

Date's

to study

No~th

base

simplification ization

recommend field,

to

data

We

the

referenced

a

and

addressed.

Development.

editor)

Is

systems not

Information

litemature

available

and

data

Evolutionary

What

are

limitations

cially

with

commercially

for

{a} a

to

spe--

Fig.

of

a data

base

parametric interactive application programmer data base administrator

external conceptual internal

I :Structure

Users PU IU APR DBA

Views E C I

APP

APP

system

[1,, 2300

"SAL

]

2800

I ... 1950

Fig. 7:Subgraphof E, MGR and SAL

I

58

same

rigor

/12~/.

a

• urnish

user

restricts fig.

W

with

to

for

the

an

the a

other

science

does

force

side

subvlew

of

can us

be

to

does

data

Since

not

base

may

which

mapped

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the

relationships~

lilustratlon,

computer not

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seen

practically

these

all to

structures

difficulty

a

{i.e.

be

conveniently

provide

subgraph)

by

the

known

some

form

from

our

to

which

user.

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structures

in

of

grephs~

high

level

it data

modeling.

2.3.

The

It

is

not

in

the

Equivalence

at

sense

all

surprising

that

in

ple

straightforward

respondin~ ween

of

the

language.

In

question

The

of

of

Bobrow

/17/,

models

are

by

Neuhold

of

creates

3 . i.

Low

Level

As

we

can

see

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as

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DBTG most

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models°

DIAM

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on

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"convenient"

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question

question will

or

are

cases

schema

decided

also

of

data

Sihley

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for

First

a

and

a

while

system.

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a

has

models /167/

for

superimpose

need

/43/.

DATA

tially

a

models

not

data

come

only

manlpule--

back

to

the

McGee

{at

model

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investigated /122/0

least

in

creates

a

A

model

on

nsuperimpositlon

results

been

a

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Rs

direction

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the

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a

it

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are

of

prob-

problem9 stated

reported

by

/82/®

3.

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Codd

EeP.

Frasson

in

/134/y to

~ even

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but

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likely

researches)

the

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eonve~%

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in

Moreoverv

to

models

the

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lem7

way

data

the

encoded

versa.

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processing

question

tion

vice

dlfferen%

"natural" a

and

equivalent

two

that

information

encoded and

CaM

o.f Data....__Mode~s

MANIPULATION

a

"low case

Versus

in

High

fig.

is

program

records in

LANGUAGES

are

of

data or

oe

access

are

LoLic

accessed

interactlvely

typically

programming level"

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retrieved

language.

as is

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in at

one

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by

type

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logic"°

the

processed

p~ocessing

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either

is

first

sequenreferred

Typical records

vla

for at

a

the tlme

59

logic". level

Research logic.

program in

allocation

subset

needs

by

a the

Even

modest

may

very

of

access compared well

plication

be

its

%he

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the

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in

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external

systems~

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going

the

specified

available

for

is

prim~rily

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the

scheduling

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the

required

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for selection

has

in

is

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the

towards

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data

the

relevance

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of

programs

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nature though

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realize

at

of

this

mappln~

primarily

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The

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Table

I lists CRMt

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IBM

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INGLES

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Table

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imple-

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60

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1971.

DBTG-Report.

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1973.

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39.

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1971.

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1970.

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Amsterdam,

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Prentice--Hall~

R.

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1974.

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74,

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1974. by

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1971.

48.

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1973

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solvability

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309, of

languages.

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1973.

relational

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95

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63.

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1975.

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Engles, view

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65.

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1975.

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the

67.

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classification

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Concurrent

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ManagemenTT April resources

241

1974. to

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1974.

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computer

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1975.

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1973.

language

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1975.

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74.

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programming

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76.

base

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Notes

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1975

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is

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L.

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1974. A

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Florentln, nal

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the

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da%a

example

processing

%ables

consis%ing

informa%ion.

by

in of

in

are %his

illustrated. query

a graphically table

skele%onsv

language

The is

pre--estebllshed in%o

which

%he

Grundlegendes

zur Speicherhierarchie

Claus Sch~nemann~

1.

IBM B6blingen

EINLEITUNG

Das Thema dieses Beitrags ist die konkrete Daten-Speicherung und -Adressierung unter Zugrundelegung eines hierarchischen Aufbaus des Speichersystems. Soweit Datenbankaspekte dabei berahrt werden~ sind sie aus der Sicht der Hardware-Implementierung

und vorwiegend unter Leistungsgesichtspunkten

gesehen. Heutige Computer-Speichersysteme

sind bereits weitgehend hierarchisch

strukturiert. Dabei soll unterschieden werden zwischen einer lediglich dutch Kapazit~tsabstufung gekennzeichneten und einer strengen Hierarchie, bei der auf jeder Stufe wahlfreier Zugriff m~glich ist und der Datenflug keine Stufe ~berspringt. Die Kombination Hauptspeicher - Pufferspeicher stellt eine strenge Hierarchie dar, bei der der Hierarchiebegriff fiberhaupt erst ins Bewugtsein ger@ckt wurde

[11. Der Pufferspeicher

(Cache) ist far die Maschinenar-

chitektur transparent und pagt die Geschwindigkeit des Hauptspeichers an die noch h~here des ~rozessors an. Ebenso ist die Folge Hauptspeicher Magnetplattenspeicher

als strenge Hierarchie anzusprechen, auch wenn

diese Betrachtungsseite

(mit Ausnahme von Programm-Paging im Rahmen des

virtuellen Speichers) bislang nicht im Vordergrund stand und der Plattenspeicher mehr als Ein/Ausgabeger~t aufgefagt und so yon der Maschinenarchitektur behandelt wurde. Der Magnetbandspeicher

ist wegen seiner langen Zugriffszeit

(incl. Band-

laden) nicht mehr im strengen Sinne zur Hierarchie zu rechnen.

115

Ans~tze,

die gro~e und billige Bandspeicherkapazit~t als echte oberste

Datenflu~-Hierarchiestufe

zu integrieren,

sind mit der j~ngeren Entwick-

lung yon automatischen Bandtransportsystemen, Kassettenspeicher,

wie z.B. beim IBM 3850-

sichtbar geworden. Dabei k6nnte beispielsweise dem

Bandspeicher die Funktion eines Archivs und dem Plattenspeicher die Funktion eines Arbeitsspeichers groSer Kapazit~t zugeordnet werden, wobei der Inhalt ganzer virtueller Plattenstapel automatisch auf Verlangen auf das Plattensystem @bertragen wird [2]. In Abbildung ] i s t

das Schema

dieses Hierarchiekonzepts skizziert. Der schwache Punkt der gegenw~rtigen Speicherhierarchie ist das Verh~Itnis der Zugriffszeiten des Hauptspeichers

zum Plattenspeicher yon mehr

als 1:1OOOO, die sog. Zugriffsl~cke. Auch ein Dazwischenschalten von Trommelspeichern bzw. Plattenspeichern mit festem Lesekopf ~ndert die Situation nicht wesentlich. Man versucht daher bekanntlich, h~Itnis durch Programmumschaltung

das Mi~ver-

im Rahmen yon Multiprogrammierung

zu

fiberbr~cken. Mit fortschreitender Prozessor- und Hauptspeichergeschwindigkeit, aber gleichbleibender Zugriffszeit der mechanisch arbeitenden Massenspeicher,

muB der Multiprogrammierungsgrad,

die Hauptspeichergr~$e

und die Zahl der Plattenspindeln immer gr6Ber werden. Damit entfernt man sich vom Kostenoptimum, au~erdem steigen die Anforderungen an das steuernde Betriebssystem und seine Komplexit~t,bei abnehmender Effizienz. Im Folgenden wird versucht,

f~r das gesamte Hierarchiespektrum die Spei-

cherparameter nach einheitlichen Gesichtspunkten zu klassifizieren und anhand solcher Parameter die Leistungsf~higkeit der Hierarchie zu diskutieren, mit besonderer Blickrichtung auf das Problem der Zugriffsl~cke. Die Anforderungen des Datenbankbetriebes werden kurz angesprochen.

2.

TECHNOLOGIE- UND OPERATIONSPARAMETER

Es sind zahlreiche Technologien bekannt, die unter Ausnutzung verschiedenster physikalischer Effekte zu sehr unterschiedlichen Speichereigenschaften f@hren. Am verbreitetsten ist heute die Halbleitertechnologie f~r die schnellen elektronischen Matrix-Speicher mit wahlweisem Zugriff und die Magnetschichttechnologie

f~r die langsameren und billigen Massen-

speicher, haupts~chlich in den Ausf~hrungen Platten- und Bandspeicher. Bine weitere Gruppe, die aber noch nicht das Stadium breiter Produktreife erreicht hat, ist die der optischen und mit Elektronenstrahl

operierenden

116

Speicher [3r4]. Auch die diversen Schieberegistertechnologien wie CCD (Charge Coupled Device)

[5,6] oder Magnetblasen (Bubbles)

[7] machen

vorerst nur tastende Schritte im kommerziellen Einsatz. Die spezifischen Arbeitsweisen der einzelnen Speicherfamilien sollen hier nicht diskutiert werdenr vielmehr wird das gesamte Speicherspektrum einheitlich durch einen Satz von invarianten technologischen und operativen Parametern beschriebenr Tabelle I. Die beiden wichtigen Operationsparameter, mittlere Zugriffszeit und Bitkostenr stehen in einer gewissen reziproken Relation zueinander. Sie bestimmen den Standort einer Technologie innerhalb des Gesamtspektrums. Im Diagramm Abb. 2 sind heutige typische Werte in Abh~ngigkeit des gewichtigsten Technologieparameters, Bitzahl pro Schreib/Lesestation, dargestellt

[8].

Die Zugriffszeit setzt sich zusammen aus der Zugriffszeit im engeren Sinner einer Art Totzeit vor der 0bertragung des ersten Bit, und der Daten~bertragungszeit. Die 0bertragungszeit ist abh~ngig yon der Datenrater gegeben durch Taktfrequenz und interne Bitbreite, und der gew~hlten ~bertragenen Blockl~nge. Zus~tzliche Verz6gerungen durch den externen 0bertragungskanal sind in der Obertragungszeit mitenthalten. Unter Modularit~t ist die Unterteilbarkeit eines Speichers bzw. einer Hierarchiestufe in Module mit eigenem parallelen Zugriff verstanden. Dadurch wird die Zugriffsrate erh~ht. Die F~higkeit zur modularen Aufteilung nimmt im allgemeinen ab mit dem Technologieparameter "Bitzahl pro Schreib/Lesestation'. Bei mechanischer Entkopplung zwischen Lesen/ Schreiben und dem Datentransport kann die Zugriffsrate dutch Oberlappung welter erh6ht werden. So wird beim Bandkassettenspeicher IBM 3850 die n~chste Kassette schon transportiert, w~hrend die vorhergehende sich noch in der Lese/Schreibstation befindet. Weitere Beispiele fur asynchronen Parallelbetrieb sind die Konfiguration mehrerer Plattenspeicher in einer DV-Anlage wie auch die Unterteilung des Hauptspeichers in unabh~ngig und parallel arbeitende Module. Auch die Bitkosten bestimmen sich in erster Linie aus der Bitzahl pro Lese/Schreibstation. Sie sind auger yon den spezifisch technologischkonstruktiven Faktoren vom allgemeinen Miniaturisierungsstand der Technik abh~ngig. Abb. 3 zeigt beispielsweise die historische Entwicklung der Bitdichte beim Magnetplattenspeicher. Entsprechend sind die Zahlenangaben

117

in Abb. 2 nur zeitbezogen zu verstehen.

Die relativen Zuordnungen dOrf-

ten hingegen weitgehend invariant zum allgemeinen Stand der Technik sein, da fortschreitende Miniaturisierung allen Technologien zugute kommt. Die Speicherkapazit~t pro Hierarchiestufe ergibt sich in einer ausgewogenen Konfiguration nach einer Art reziproker Funktion der jeweiligen Bitkosten Ein weiterer operativer Parameter ist die Zuverl~ssigkeit des Speichers, d.h. die mittlere Zahl yon gelesenen Bits pro fehlerhaftem Bit. Dieses Merkmal ist eine Funktion der natOrlichen Fehlerfreiheit des Mediums, des Sortierungsgrades nach guten Einheiten und des Aufwands an gezielter Redundanz mit nachfolgender Fehlerkorrektur. Die Fehlerdichte des Mediums nimmt n a t u r g e m ~

mit der Homogenit~t ab. Typische Zuverl~ssigkeitswerte

sind (nach entsprechendem Sortierprozess) z.B. beim fabrikneuen Plattenspeicher 10 9 und 1012 nach erfolgter Korrektur. Die physikalische Natur der Speicherung bestimmt den Grad der Fl~chtigkeit der eingeschriebenen Information. Bei einem Arbeitsspeicher kann man eine gewisse Fl@chtigkeit mit periodischem Wiederauffrischen zulassen, bei einem Archiv- oder Journalspeicher mud nat~rlich ein dauerhaftes Speichern gefordert werden. In gewisser Verwandtschaft

zur FiOchtigkeit steht die Eigenschaft des

ON-line oder OFF-line Einschreibens, ROM verstanden.

letzteres auch allgemein unter

Bei verschiedenen Anwendungen,

kumenten mit geringer ~nderungsfrequenz,

z.B. Speicherung yon Do-

kann der ROM-Speicher durchaus

sinnvoll und, da entsprechend billig, von Interesse sein. Ein Obergang zwischen dem normalen schreibbaren Speicher und dem ROM stellt der PROM bzw. EAROM (Programmable bzw. Electrically Alterable Read Only Memory) dar. Der ROM-Speicher wird bier nicht weiter behandelt. Der letzte Operationsparameter

ist die adressierbare Einheit, die im

Verein mit der eigentlichen Zugriffszeit die Komplexit~t der Zugriffsmethode und Effizienz des Datensuchens bestimmt. Man unterscheidet zwischen Orts- und Inhaltsadressierung. sierung ist auf Hauptspeicherebene

Die Ortsadres-

die dominierende Adressierungsart:

Die physische Lokation jedes Datenelementes ist vom Programm definiert und wird Ober die Adresse direkt gefunden. Dieses Konzept ist auf den h6heren Speicherebenen f~r das Aufsuchen yon Datens~tzen nicht mehr zweckm~6ig, wenn die S~tze z.B. in Form einer Datenbank organisiert,

118

programmunabh~ngig und vielen Benutzern verf~gbar sein sollen. Sie m~ssen also letztlich durch ihren Inha!t, gegeben durch ein oder mehrere Merkmale, gekennzeichnet sein. Innerhalb eines Satzes sind die Daten im allgemeinen wieder formatiert, d.h. ihre semantische Bedeutung ist durch ihren relativen Ort bestimmt. Die heutige Suchtechnik bei inhaltsadressierten Datens~tzen bedient sich Indextabellen,

in denen z~B. die Hauptmerkmale numerisch oder alphabe-

tisch geordnet und die reale Speicheradresse direkt zugeordnet ist. Beim Vorliegen weiterer

(Neben-) Merkmale k6nnen diese in eigenen Ta-

bellen gelistet werden, wobei die Speicheradressen aller S~tze, die dieses Merkmal enthalten, wieder zugeordnet werden. Mit diesen invertierten Listen kann bekanntlich der Prozess des Suchens nach mehrfachen Merkmalen schnell, d.h. ohne alle S~tze sequentiell prozessieren zu m~ssen, durchgef~hrt werden. Mit Hilfe der Indextabellen wird also die Inhaltsadresse eines Datensatzes

in eine Ortsadresse umgewandelt.

Letz-

tere wird dann beim Speichern mit wahlfreiem Zugriff schnell und direkt angesteuert. Das Durchsuchen der Indextabellen nach dem gew@nschten Merkmal stellt in sich nun wiederum einen Proze~ mit sequentieller Schrittfolge dar. Ein weiteres Parallelisieren w~re das Abspeichern der Indextabellen in Assoziativspeichern,

mit folgenden Vorteilen:

Fortfall der numerischen oder alphabetischen Merkmalsordnung. Dadurch einfache Aufarbeitung durch direktes Zuf~gen/Entfernen neuer Indizes. Fortfall der invertierten Listen, da gleichzeitig auf mehrfache Merkmale assoziiert werden kanno Direktes gleichzeitiges statt sequentielles Suchen. Die Eigenart des Assoziativspeichers,

eine Formatierung der Daten zu

verlangen, w~re in diesem Fall kein Nachteil. Ein Sonderfall der Ortsadressierung

ist die Adressierung mit Zeigern.

Dabei wird auch eine Entkopplung yon Benutzerprogramm und Datenadresse erreicht. Nachteilig ist das sequentielle Durchlaufen der Zeigerkette. Die einzelnen Speichertechnologien unterscheiden sich nun hinsichtlich der GrS~e der h a r d w a r e - m ~ i g

adressierbaren Einheit. Diese ist z.B. ein

119

Byte beim (Halbleiter-) Matrixspeicher,

ca. 10-20 KBytes beim Platten-

speicher und Millionen yon Bytes beim konventionellen Bandspeicher. Wenn diese adressierbare Einheit nun gleich oder kleiner als die gewfinschte zu fibertragene Blockl~nge ist, soll von wahlfreiem Zugriff gesprochen werden. Der Plattenspeicher hat nur einen semi-wahlfreien Zugriff, da seine Adressiereinheit

(die Spur) um ein Vielfaches grS~er als eine bequeme

logische Satzl~nge bzw. eine ffir diese Hierarchiestufe optimale Blockl~nge ist. Der konkrete Block mu~ dann wieder sequentiell auf der Spur gesucht werden. Die sogenannten Zugriffsmethoden,

also die praktischen Prozeduren zum

Aufsuchen von Datens~tzen spiegeln die jeweils zugrundeliegenden technologischen Adressierparameter wider. Ein Beispiel ist die index-sequentielle Zugriffsmethode ffir "direkten wahlfreien" Zugriff zum Plattenspeicher:

Dabei sind die Hauptmerkmale

der Datens~tze in einer Indextabelle nach aufsteigender Ordnungszahl geordnet. Die Tabelle ordnet jeweils einer Gruppe von S~tzen die zugeh~rende Spuradresse auf der Platte zu° Auch die S~tze selbst sind nach der gleichen Ordnungszahl geordnet, um im Falle sequentiellen Zugriffs die gro~e Zugriffszeit ffir jeden individuellen Satz zu eliminieren. Beim Rotieren der Platte werden die ausgelesenen Satzmerkmale mit dem Suchmerkmal verglichen, his 0bereinstimmung herrscht. Beim Aufarbeiten,

z.B.

Zuffigen eines weiteren Satzes in die m6glicherweise physisch lfickenlose Satzfolge, weist ein Zeiger zu einer neuen Spuradresse auf einer 0berlaufspur. Die Methode kombiniert also die Suchelemente Indextabelle, sequentielles Suchen und Zeigertechnik zu einer den spezifischen Plattenspeicherbedingungen angepa~ten Prozedur, Abb. 4a. Bei einem anderen Speicher mit auch homogenem Medium, dem Elektronenstrahl-Speicher,

ist die Adressiereinheit

frei w~hlbar zwischen einem

und Zehntausenden yon Bytes. Das Zugriffsverfahren kann rein indexorientiert und entsprechend einfach gehalten werden: Das sequentielle Suchen entf~llt. Ein 0berlaufproblem existiert nicht. Dank der kurzen eigentlichen

(elektronischen)

Zugriffszeit kann auf eine sequentielle

Satzordnung verzichtet und der Satz an beliebiger Stelle gespeichert werden, Abb. 4b. Die gr6~ere Adressiereinheit,

d.h. die geringere "Wahlfreiheit", bei

!20

den kosteng~nstigen Technologien ist an sich kein prinzipieller Nachteil, da innerhalb einer Hierarchie ohnehin mit Block@bertragung gearbeitet wird. Ein gradueller Nachteil ist nur dann festzustellen, wenn wie beim Plattenspeicher optimale Blockl~nge und technologische Adressiereinheit nicht ~bereinstimmen.

Diese Diskrepanz schl~gt sich dann in aufwendigen

und zeitraubend ab!aufenden "Zugriffsmethoden" nieder.

3.

SPE ICHERHIERARCHIE

Aufgabe eines Speichersystems

ist neben der Speicherung,

dem Prozessor

die ben6tigten Daten in gen~gend kurzer Zeit und in der angeforderten Menge pro Zeiteinheit zur Verf@gung zu stellen. Analog zu den SystemLeistungsparametern Antwortzeit und Durchsatz l ~ t

sich die Speicher-

leistung durch die Parameter Zugriffszeit und Zugriffsrate definieren. Wenn ein Speicher nur einen Zugriff gleichzeitig gestattet,

kann die

Zugriffsrate etwa gleich dem reziproken Wert der Zugriffszeit gesetzt werden. Bei gleichzeitig mehreren Zugriffen,

d.h. Modularit~t gr6~er

als I, erh~ht sich die maximale Zugriffsrate entsprechend. Wie weir die maximale Zugriffsrate ausgenutzt werden kann, h~ngt yon Parametern wie Systemsteuerung,

Programmprofil, Multiprogrammierungsgrad

und Zahl der

Parallelprozessoren etc. ab. In einer Hierarchie

ist eine gewisse Grundmodularit~t der einzelnen

Stufen schon im Interesse eines gleichzeitigen Datenverkehrs nach oben und unten w~nschenswert.

Dies wird steuerungsm~6ig z.B. auf Hauptspeicher-

ebene durch das unabh~ngige Operieren yon Prozessor und Kan~len erreicht. F~r effektive Multiprogrammierung tenspeicherstufe

ist ausreichende Nodularit~t der Plat-

zwingend Voraussetzung.

Zweck der Multiprogrammierung

ist es, die resultierende Zugriffsrate - gemessen an der Schnittstelle zum Prozessor - und damit den Systemdurchsatz

zu erh6hen.

Bekanntlich liegt dessenungeachtet der Engpa~ f@r den Durchsatz heutiger DV-Systeme immer noch bei der Zugriffszeit und Zugriffsrate des Plattenspeichers. Da weitere Geschwindigkeitsfortschritte Halbleiterspeicher

f@r Prozessor und

in Zukunft durchaus erwartet werden d~rfen, die Plat-

tenspeicher-Zugriffszeit

abet kaum noch verbesserungsf~hig ist, wird

dieses Problem immer dr~ngender: Multiprogrammiergrades,

Eine L~sung Qber weitere Erh6hung des

d,h. der Zahl der gleichzeitig operierenden

Programme, mit entsprechender Erh6hung von H a u p t s p e i c h e r g r ~ e tenspeichermodularit~t

und Plat-

erscheint aus Kosten- und Komplexit~tsgrfinden

121

unpraktikabel. Au~erdem leidet bei zu hohem Multiprogrammierungsgrad die Effizienz: Die Systemverwaltung nimmt relativ zur Wirkarbeit zu, die Chance, mit einer Plattenarmposition mehrfache Zugriffe abzudecken, nimmt ab usw. Eine andere L6sung dieses Problems ist der weitere Ausbau des Speicherhierarchiekonzeptes,

bei beschr~nktem Multiprogrammierungsgrad.

(nicht realisierbare)

Der

ideale Speicher, d.h. der Speicher mit der Zu-

griffszeit des Pufferspeichers und den Kosten des Bandspeichers, l ~ t sich durch eine ausgewogene Hierarchie mit gen@gend feiner Stufung ann~hern. Gl~cklicherweise verspricht die technologische Entwicklung Speicherprodukte, die leistungs- und k o s t e n m ~ i g

gerade das Gebiet der "L~cke" aus-

f~llen und sich so gut in das Spektrum einf~gen. M~gliche Technologien f~r die "L@cke" sind z.B. der CCD-Schieberegisterspeicher,

der Schiebe-

registerspeicher mit verschiebbaren magnetischen Blasen (Bubbles) sowie die Elektronenstrahlspeicherr~hre,

Abb. 5. Diese Technologien sollen im

Folgenden elektronische Massenspeicher genannt werden.

3.1

Hierarchiemechanismus

Die Speicherhierarchie besteht also aus der Hintereinanderschaltung yon Speicherstufen, wobei mit zunehmender Stufenordnungszahl

die Zugriffszeit

und Speicherkapazit~t zunimmt. Bei einem Speicherzugriff des Prozessors versucht dieser zun~chst, die Daten auf der untersten schnellsten Ebene zu finden. Bei Mi~erfolg wird zur n~chsten Ebene zugegriffen und so fort. Bei einer Daten@bertragung auf die jeweils niedere Ebene wird nun nicht nur das verlangte Wort oder Byte, sondern gleich ein ganzer Block ~bertragen. Auf jeder unteren Ebene wird ein

Teil

des Blocks abgelagert.

Die 0bertragungszeit ist bei den gew~hlten Blockl~ngen meist klein gegen die eigentliche Zugriffszeit. Das Wesen der Speicherhierarchie dr~ckt sich also darin aus, da~ unter Zulassung yon geringfOgig mehr Zugriffszeit (n~mlich incl. 0bertragungszeit) @bertragen werden,

ganze Daten- oder Programmbl6cke

in der Annahme, da~ davon ein Yell in n~chster Zukunft

ohnehin zum Prozessieren angefordert wird. Es liegt also ein prophylaktischer Zugriff (look ahead) unter Ausnutzung der (gegen die eigentliche Zugriffszeit) kurzen 0bertragungszeit vor. Unterst@tzt wird dieser Mechanismus dadurch, da~ die Daten oftmals in kurzem Zeitraum mehrfach zugegriffen werden,

z.B. bei Programmschleifen,

abet auch beim Operieren

122

auf h~ufig benutzte Arbeitsdaten Die Trefferrate, gegriffenen Ebene,

d~ho die Wahrscheinlichkeit,

Ebene anzufinden,

ferner im allgemeinen

sie nat~rlich

folgt im einfachsten

kann selbstverst~ndlich

bei denen jeder Zugriff software-implementiert

Datenteile

und entsprechend

Einspeichern z.B.

usw. Auf den h6heren Ebenen, eingeht,

ist die Steuerung

"intelligenter".

fiber einen das Gesamtspeichersystem

L~fassenden

erfolgen. enthielte

ordnung der virtuellen

Entwicklung

in einer Speicherhierarchie: speicheradresse Hauptspeicher

gibt es meist mehrere Adressr~ume wird die reale Haupt-

Platz im Pufferspeicher

Indextabellen

umfa~t,

Zu-

zur lokalen Ebenenadresse.

Auf Pufferspeicherebene

einem bestimmten

den inhaltsadressierten

der realen Adresse

h6heren Hierarchiestufen die Datenlokalisierung:

zugeordnet.

Beim

die also bereits zugeerdnet.

Bei

fibernehmen die vorer-

Logisches

und hierarchie-

Suchen wird identisch.

Die Zuordnungstabellen Ebenen gespeichert~

werden

Beim

entweder auf der gleichen oder auf unteren

(schnellen)

einem eigenen mehr oder weniger

Pufferspeicher

assoziativ

eines Archivspeichers~

der alle Daten im 0N-line

einen magnetischen

Bandspeicher

und einem Prozessorsystem, und einer Hierarchie

wird die Tabelle

arbeitenden

Man kann sich so das gesamte DV-System vorstellen spielsweise

fQr die dynamische

wird die heute meist virtuelle Adresse,

einen grS~eren Adressraum

spezifisches

dann eine Tabelle

Gesamtspeicheradresse

Aufgrund der histerischen

transport~

Algo-

Dieser Mechanismus

in untere schnelle Ebenen,

im Hauptspeicher

er-

und das Suchen yon Daten auf einer Ebene kSnnte kon-

Jede Hierarchiestu£e

Prozessor

h~ngt

ab.

nach den gebr~uchlichen

(Least Recently Used).

in die Leistungsbilanz

zeptuell am einfachsten

w~hnten

dieser

Davon unabhgngig

unterstfitzt werden durch residentes

Teile des Betriebssystems

zu-

auf einer geffillten Hierarchiestufe

Fall selbstregelnd

gewisser hgufig gebrauchter

Die Adre~steuerung

zu mit der Speicherkapazit~t

Daten- und Programmprofil

yon Speicherplatz

rithmen wie FIFO oder LRU

Adressraum

nimmt

Kataloge usw.

Daten auf der jeweils

mit der Blockl~nge.

vom jeweiligen

Das Freimachen

wie Indextabellen~

Speicher

in

gehalten.

als die Kombination Zugriff enth~it,

mit automatischem

bei-

Band-

das wiederum aus dem eigentlichen

yon Arbeitsspeichern

besteht.

Die vet-

123

schiedenen,

teilweise im vorigen Abschnitt diskutierten Technologie-

und Steuerungsparameter variieren entlang der Hierarchieachse wie in Abb. 6 skizziert.

3.2

Leistungsbetrachtung

Das wichtigste Kriterium der Speicherhierarchie ist die Gesamtzugriffszeit bzw. Gesamtzugriffsrate,

absolut gesehen als auch kostenbezogen.

Diese Zusammenh~nge sollen im folgenden anhand eines sehr einfachen Modells diskutiert werden. Das Modell orientiert sich an "typischen" Werten f@r die verschiedenen Parameter und extrapoliert bei nicht bekannten Daten. Wie das Technologiediagramm Abb. 2 bereits indiziert, scheint eine nat~rlich einfache G e s e t z m ~ i g k e i t

zwischen den Bitkosten und der Spektrums-

variablen Zugriffszeit zu bestehen. Diese und die Zuordnung der Trefferrate und Speicherkapazit~t diagramm Abb. Gerade

zur Zugriffszeit sind im Modellparameter-

7 aufgetragen. Die Kapazit~tsverteilungskurve

ist als

(im log. Ma~stab) angenommen, mit den Endpunkten Puffer- und

Archivspeicher. Die gew~hlte Archivkapazit~t ist 1012 b, die Pufferkapazit~t 200 Kb. Die auf der Geraden liegenden Punkte f@r Haupt- und Plattenspeicher entsprechen etwa realen Werten. Die Kapazit~tsverteilungskurve ist an sich nat@rlich innerhalb des technologisch verf~gbaren Spektrums frei w~hlbar. Mit wachsender Prozessorleistung und Datenmenge wird sie nach oben verschoben werden. F~r die Trefferrate im multiprogrammierten Stapelbetrieb liegen als Funktion der Kapazit~t und Blockl~nge einige Erfahrungsdaten im Bereich Puffer - Hauptspeicher vor [9]. Typische Werte daf~r wurden der Modellkurve zugrundegelegt.

Zu den oberen Hierarchieebenen ~in wurde extrapoliert.

Das Modell ber~cksichtigt nicht die gegenseitigen Abh~ngigkeiten von Blockl~nge,

Zugriffszeit, Trefferrate, Multiprogrammierungsgrad usw.,

sondern nimmt starr typische Werte an. Die Gesamtzugriffszeit ist

tges = t1+(1-hl)t2+(1-h2)t3 + .... (1-hn_1)t n

GI. I

124 mit tn ~ Zugriffszeit der n-ten Stufe hn = T r e f f e r r a t e

der n-ten Stufe

Die maximale Gesamtzugriffsrate,

d.h. der Zugriffsflu~ an der Schnitt-

stelle zum Prozessor ist I

max° Zges = tt

1,hl

GI. 2

l_,hn_l

P-~I+ - ~ 2 t2+ . . . .

Pn

tn

mit Pn = Zugriffsparallelit~t auf der n-ten Stufe. Die Zugriffsparallelit~t entspricht in etwa der Modularit~t. angenommen, da~ 50% der Zugriffsparallelitgt

Es wird

sich jeweils in echter

Erh~hung der Zugriffsrate durch Multiprogrammierung niederschlagen, Peff also 0,5 po Ferner, da~ unterhalb der Plattenspeicherebene Programmumschaltung nicht mehr lohnt (p=1) und schlie~lich,

da~ Einzel-

Prozessorbetrieb vorliegt. GI. 2 modifiziert sich dann entsprechend. Einige Modellergebnisse auf der Grundlage realer Technologien sind in Tabelle II zusammengestellt.

Unterschiedliche

Speicherzugriffsraten

schlagen sich in unterschiedlicher Prozessorauslastung nieder. Es wurde ein Modeilprozessor mit 2 MIPS (Millionen Instruktionen pro Sekunde) und durchschnittlich

2 Zugriffen pro Instruktion gewghlt. Dieser Pro-

zessor kann seine volle Leistung nur entfalten, wenn das Speichersystem 4 Millionen Zugriffe pro Sekunde z u l ~ t . Die schlechte Auslastung dieses 2-MIPS-Prozessors bei heutiger Konfiguration ohne Multiprogram~ierung ~berrascht nicht. Auch mit Multiprogrammierung ist die Auslastung nur mg~ig. Erst die Einf@hrung des elektronischen Massenspeichers erbringt eine Verbesserung auf eine vern@nftige Gr6~enordnung.

Bei Multiprogrammierung

verlagert sich jetzt der Engpa~ f@r die Zugriffsrate vom Plattenspeicher (mit seiner hohen Modularit~t)

zum Bandspeicher. Dieser Engpa~ k6nnte

~berwunden werden durch weitere Erh6hung der Hierarchiestufenzahl,

kon-

kret durch Einbau einer Zwischenstufe zwischen Platten- und Bandspeicher.

125

Technologisch liegt eine solche Stufe im Bereich des Sichtbaren, n~mlich ~ber eine Modifizierung des konventionellen Plattenspeichers

zu

einem Satz yon flexiblem Platten mit sehr hoher Bit-Volumendichte

[9].

Die Zugriffsrate der Hierarchiekonfiguration

liegt dann oberhalb yon

4 Millionen pro Sekunde. Die Ergebnisse aus Tabelle II werfen die Frage nach der optimalen Hierarchiestufung auf, bei festgehaltenen Endpunkten.

Ffir diese Analyse wird

ohne Bezug auf reale Technologien eine g l e i c h m ~ i g e

Stufung vorgesehen

und die Stufenzahl variiert. Multiprogrammierung wird jetzt nicht ber@cksichtigt. Ergebnisse sind in Abb. 8 aufgetragen:

Bei ca. 16 Stufen

stellt sich ein Sgttigungswert fur die Zugriffsrate ein (die in diesem einfachen Fall der reziproke Wert der mittleren Zugriffszeit ist). Diese Zugriffsrate ist nur etwa 2 mal kleiner als die der reinen Pufferspeicherstufe. In Abb. 8 ist weiterhin die Preisleistungszahl, pro Gesamtbitkosten,

n~mlich Zugriffsrate

aufgetragen.

Hier liegt das Optimum bei ca. 8-10 Stufen. Die Verbesserung gegenfiber einer 4-stufigen Hierarchie ist g r ~ e r

als Faktor 6. Auf der Grundlage

der realeren Daten in Tabelle II ist der Gewinn bei einem Schritt von heutigen 4 Stufen auf (die durchgespielten)

6 Stufen noch wesentlich

h6her, da dort nicht von einer gleichmg~igen Stufung ausgegangen wurde. Ein weiterer Vorteil der feineren Hierarchiestufung ist die Verbesserung des Prozessor-"Wirkungsgrades":

Die Zahl der Zugriffe zum Platten- und

Bandspeicher nimmt ab. Damit nimmt auch die Zahl der prozessierten Instruktionen

(der Zugriffsroutinen) pro Zugriff zur Speicherhierarchie

ab, und der Prozessor-"Wirkungsgrad"

nimmt zu. Schlie~lich kann das Be-

triebssystem einfacher gehalten werden. In diesem Modell ist der Zuverl~ssigkeitsaspekt nicht enthalten, der mit wachsender Stufenzahl kritischer wird. Ebenso sind die Kosten der Steuerungen, Adresstabellen, Trefferratenkurve

etc. nicht ber@cksichtigt.

Die Extrapolation der

ist v611ig hypothetisch. All dessert ungeachtet d~rfen

die Modellergebnisse als Indiz daffir verstanden werden, dab eine feinere Hierarchiestufung noch erhebliches Leistungspotential

enth~it.

126

4.

SPEICHERASPEKTE BEI DATENBANKBETRIEB

Auch der Datenbankbetrieb kann grunds~tzlich in die bisherige Modellbetrachtung eingenordnet werden° Derjenige Parameter, der sich m~glicherweise

(in Richtung ungQnstiger Werte) ~ndert, ist die Trefferrate,

insbesondere auf den hohen Ebenen. Erfahrungen dar~ber m@ssen abet erst gewonnen werden, sodag hier die Modellwerte beibehalten werden,

zumal

auch bei der Datenbank ein gewisses "Nachbarschafts"-Verh~Itnis

yon

Anfragen festzustellen sein dQrfte. Praktisch-anschaulich k~nnte man sich eine Funktionsverteilung

auf die einzelnen Hierarchiestufen wie in

Tabelle III skizziert, vorstellen. Zugriffsrate m~ssen v o n d e r

Datengruppen mit hoher professioneller

Archivstufe auf die Plattenspeicherstufe

resident ausgelagert werden. Der spezifische Datenbank-Leistungsparameter die zul~ssige Anfragenrate.

ist, neben der Datenmenge,

Diese sollte mit wachsender Datenbankkapa-

zit~t auch ansteigen. Die folgende 0berschlagsrechnung m~ge einige Veranschaulichung bringen: Nach Tabelle II ist bei heutiger Hierarchie und Multiprogrammierung die Modellzugriffsrate

~85 M/s. Wenn wir einen Programmablauf von durch-

schnittlich 100 K Instruktionen pro Datenbank-Anfrage

annehmen, w~rde

das System 4.25 Anfragen pro Sekunde erlauben. Dieser Wert dfirfte bei einer Datenbank-Kapazit~t yon 1012 b nicht ausreichen. Nach BinfQhrung des elektronischen Massenspeichers

erh~ht sich die Anfragenrate auf 14

pro Sekunde, Mit einer zus~tzlichen Zwischenstufe zwischen Platten- und Bandspeicher erh6ht sie sich auf ca. 30 pro Sekunde - entsprechende Prozessorleistung von ca. 3 MIPS vorausgesetzt. Die letzten Endes interessierende Frage, wieviele Terminals an eine Datenbank dieser Gr6ge bei befriedigender Bedienung angeschlossen werden k6nnen, h~ngt natQrlich yon der mittleren Anfragelast pro Terminal ab. Bei einer angenommenen mittleren Last yon einer Anfrage pro Terminal und Minute errechnet sich eine Terminalzahl von 30.60=1800. Diese Anschlugm6glichkeit pro 1012 b Datenbankkapazit~t

erscheint ausreichend.

Als Schlugfolgerung aus diesen Betrachtungen soll die Feststellung getroffen werden, dag Organisation und Technologie zukQnftiger Speichersysteme das Potential haben, den Leistungsanforderungen eines breiten Datenbankbetriebes

gerecht zu werden.

127

Literatur [ I] C.W. Pugh, "Storage Hierarchies:

Gaps, Cliffs and Trends",

IEEE Transactions on Magnetics, Vol. Mag-7, No. 4, Dez. 1971 [ 2] C. Johnson, "IBM 3850-Mass Storage System", Nat. Comp. Conf.

1975, S. 509

[ 3] J. Kelly, "The Development of an Experimental Electron-BeamAddressable Memory Module", Computer, Februar 1975 [ 4] W.C. Hughes et. al., "BEAMOS, A New Electronic Digital Memory", Nat. Comp. Conf. [ 5] G.F. Amelio,

1975, S. 5-41

"Charge-Coupled Devices for Memory Application",

Nat. Comp. Conf. 1975, S. 515 [ 6] W.S. Boyle et. al., "Charge-Coupled Devices - A New Approach to MIS Device Structures", IEEE Spectrum, Juli 1971, S. 18 [ 7] A.H. Bobeck et. al., "A New Approach to Memory and Logic: Cylindrical Domain Devices", Proc. AFIPS Conf., Vol. 55, 1969 [ 8] R.R. Martin et. al., "Electronic Disks in the 1980's", Computer, Februar 1975, S. 24 [ 9] D.H. Gibson, "Considerations

in Block-Oriented Systems Design",

AFIPS Proc., Vol. 30, SJCC 1967, S. 75-80

128

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SPEICHERMEDIUM (HOMOGENIT~T, BITDICHTE)

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STUFENZAHL

System R:

A Relational Data Base.Management System

Morton M. Astrahan, IBM Research Laboratory, San Jose, California Donald D. Chamberlin, IBM Research Laboratory, San Jose, California W. Frank King, IBM Research Laboratory, San Jose, California Irving L. Traiger, IBM Research Laboratory, San Jose, California INTRODUCTION System R is a data base management system which provides a high-level, non-procedural relational data interface. The system provides a high level of data independence by isolating the end user as much as possible from underlying storage structures. The system permits definition of a variety of relational views on common underlying data. Data control assertions,

features

are

also

provided,

including

authorization,

integrity

triggered transactions, a logging and recovery subsystem, and f a c i l i t i e s

for maintaining data consistency in a shared-update environment. The relational model of data was introduced by Codd [ I ] in 1970 as an approach toward providing solutions to the various outstanding problems of current data base management systems. In particular, Codd addressed the problems of providing a data model

or view which isdivorced from various implementation considerations (the data

independence problem) and also the problem ofproviding the data very

high-level,

non-procedural

stressed here that the relational model is a framework compatible

solutions

to

base user with

data sublanguage for accessing data.

these and other

or

problems in

philosophy

a

I t should be for

finding

data base management; the

relational approach is thought to make solutions more elegant and perhaps simpler but the

approach by i t s e l f does not solve these problems.

With this caveat in mind, our

f i r s t purpose is to b r i e f l y describe a related set of data base problems which we are attempting to solve in a coherent way following the relational approach. Our solutions are embodied in an experimental prototype

data

management system called

System R which is currently being designed, implemented, and evaluated at the IBM San Jose Research Laboratory. We wish to emphasize that System R is a vehicle for research in data base architecture, and is not available as a product. Furthermore, the ideas discussed in this paper should not be considered as having product implications.

140 To a large extent, the acceptance and value of the relational approach hinges on the demonstration that a system can

be b u i l t

which is

operationally

complete (can

actually be used in a real environment to solve real problems) and has performance at least comparable to today's existing systems.

With the

present

state

of

systems

performance prediction, the only credible demonstration is to actually construct such a system, and to evaluate i t in a real environment.

The point of this

paper,

then,

is to describe the set of problems which are being studied in the System R framework, to discuss the objectives of the system (which amounts to a description or definition of

the term operationally complete), and to describe the architecture of the system,

including overall structure, interfaces, and functional design. The System R project is not the f i r s t however, we know of complete capability. related

no other

implementation

hence data

the

relational

Other efforts have demonstrated f e a s i b i l i t y in various

problem areas.

these

of

projects

the

No concurrent sharing of data was permitted

control, locking, and recovery issues were greatly simplified.

INGRES project [4] at U.C. Berkeley is also single-user oriented. of

approach;

For example, both the IS/I system [2] and the Phase/O SEQUEL

prototype [3] were single-user systems. and

of

system which is r e a l l y aimed at an operationally

In addition,

The each

has an incomplete treatment of views, i . e . , of providing various

views of data to various users. The next section describes the overall goals of System R and describes capabilities

which we believe

the

list

to be necessary in an operational environment.

of The

following section describes the architecture of the system, and describes in overview terms i t s major interfaces and the components which support these interfaces SYSTEM OBJECTIVES System R is focused on f i v e main goals: I.

To provide a high l e v e l , non-procedural relational data interface.

2.

To provide the maximum possible data independence for

the

basic

data

objects

(base relations). 3.

To support derived relational views.

4.

To provide f a c i l i t i e s for data control consistent with the high level of the data interface.

5.

To discover

the

performance trade-offs

inherent

in

this

type of data base

capability. F i r s t , each of these goals w i l l be discussed and i l l u s t r a t e d . I. High Level Non-Procedural Relational Data Interface The trend toward higher level languages has long been evident in the programming

141 domain.

Set-oriented

data

Information Algebra [5].

sublanguages were introduced

in

1962 in the CODASYL

Codd's ALPHA language [6] and Relational Algebra [7] raised

the level of data sublanguages by letting the user specify the properties of the data required without describing the access Path or detailed sequence of operations to

be

used to obtain the data. This trend toward higher level non-procedural programming [8] is aimed at reducing the number of decisions the programmer must make in order to express his problem/solution, and at making the decisions more relevant to the solution (as opposed to being relevant to the programming of a specific computer). Halstead

has examined two programs solving

the

same problem using his software

physics techniques [9], one written in ALPHA and the other in DBTG-COBOLand for this case found that the ALPHA solution required 30 times fewer mental discriminations than the lower level solution This observation should be directly translatable into increased

programmer productivity and ease of maintenance.

is one strong reason for the goal of supporting

Thus, human productivity

a high-level,

non-procedural

data

interface. The other reason for moving in the direction of non-procedural interfaces is related to the optimization of the execution of the program. to

I f the data base were dedicated

a single application, its structure could be optimized for that application only,

and the application could be written in terms of that optimized structure. in

an integrated

inefficient.

data

Hence, the

application

on a data

applications.

base environment,

application intent optimization.

such local optimization is l i k e l y to be

system must i t s e l f

optimize

base whose structure

The non-procedural, and hence is

is

high-level easier

the

execution

for

rather

much mathematical

the

sophistication

better

system to

algebra

projection,

join,

introduces division,

a collection etc.)

relational results. The need to relational languages became apparent research groups [11,12].

which

of

each

on the aggregrate

have relational

reveals

the

use as a basis for

part

particular, the ALPHA language is based on the f i r s t order predicate relational

of

a compromise among the various

specification

The available relational languages (ALPHA, Relational Algebra) were very required

However,

formal

of the user. calculus.

and In The

operators (selection, operands and produce

discover more user-oriented, non-mathematical and is currently being pursued by several

The principal external interface of System R is called the Relational Data Interface (RDI), and provides relationally complete [7] f a c i l i t i e s for data manipulation, data definition, and data control. To support high-level, non-procedural~ set-oriented applications, the RDI contains the SEQUEL data sublanguage in its entirety. SEQUEL is documented in [I0].

142 Of course, not a l l requirements can best be met through a non-procedural approach and f o r this reason the RDI

contains

single-tuple-oriented

operators

(FETCH, INSERT,

DELETE, REPLACE, e t c . ) in addition to the set-oriented c a p a b i l i t i e s of SEQUEL. We have designed the RDI to be used in two modes: (a) D i r e c t l y by an application

program

(e.g.,

a

COBOL program)

which

uses RDI

operators to access the data base. (b) As the target of a t r a n s l a t o r program (a special case of an application

program)

which is emulating some other type of user interface. 2.

Data Independence

Date [13] has defined data independence as the immunity of applications to change storage structure and access strategy.

the a b i l i t y of a data base system to provide various logical views of the data for

example to make v i s i b l e only selected records of a f i l e ,

of each record. application

By view,informally we mean a

can

access

the

data

base.

relational

The

to

distinguish

window through

which

an

term "window" is used to imply that the

these two notions of data independence.

address the only f i r s t

base;

and selected a t t r i b u t e s

changes to the data base which a f f e c t the view are v i s i b l e to wish

in

Often, however, the notion is associated with

application.

We

In t h i s subsection we

notion of data independence; the second~ which

we call

the

support of derived views, is discussed in the next subsection. Typically,

data

management systems permit two levels of data d e f i n i t i o n .

The lower

l e v e l , or "schema", describes the p r i m i t i v e data objects being managed by the system. In System R, these p r i m i t i v e objects are called base relations.

The description of a

base r e l a t i o n includes the r e l a t i o n name, a t t r i b u t e names, description of

the

units

of each a t t r i b u t e , the domain of each a t t r i b u t e , the order of the a t t r i b u t e s within a r e l a t i o n , the order ( i f any) of the tuples within a r e l a t i o n , the

definition

of

a

base table

storage or available physical access paths to the data. has

a very

direct

etc.

In

particular,

does not include any information about physical However, each base r e l a t i o n

physical representation, i . e . , each tuple of the r e l a t i o n has a

stored representation.

Data independence implies

that

the

base

relation

can

be

supported by a v a r i e t y of physical structures and access strategies. Clearly

data

independence

is important i f a system is to allow growth and meet the

changing requirements of various applications. access structures. 3.

System R provides

a

rich

set

of

Any of these can be used to support a given base r e l a t i o n .

Support of Derived Views

The higher level of data independence consists of the a b i l i t y to define a l t e r n a t i v e views in terms of the p r i m i t i v e data objects. This notion appears in most

143 contemporary data management systems and the usefulness of such systems depends in large measure on the capability of the system to support derived views. The i n a b i l i t y to support views which d i f f e r from the primitive views often leads to programs which are complex, because they are warped to use views which are not natural but can be supported, and which require extensive maintenance as changes over time.

the

system

As an example of the usefulness of derived views, consider a data base containing the following

two

types

of

records:

CATALOG (PARTNO,DESC,PRICE) and

SALES

(SALENO,PARTNO,QSOLD). The CATALOG f i l e is ordered by part number, and gives the description and price of each part. The SALES f i l e is ordered by sale number, and gives the part number and quantity sold for each sale. Suppose we wish to print out all the SALES records for parts which have a price greater than $I000. We could write a program to scan through the CATALOG f i l e , finding parts $I000;

for

with

PRICE>

each such part, a separate scan could be made through the SALES table to

find all the corresponding records.

This program would

be highly

procedural;

it

would require repeated scanning of the SALES table, and would give the system l i t t l e opportunity to optimize the query by choosing among alternate access paths. However, i f our system permits the specification of derived views, the user might specify a view consisting of the join of the two f i l e s , as follows: SALES-CAT (SALENO,PARTNO, DESC,PRICE,QSOLD). The program could then consist of a single through

the

SALES-CATview.

the system f l e x i b i l i t y

to take

scan

Besides being easier to write, this program would give advantage

of

new access paths

which

may become

available (such as a PARTNOindex on the SALES f i l e ) without requiring changes in the program. A major goal of the System R project is to develop and investigate the technology derived views. studied:

This

problem has

three

of

distinct aspects, each of which is being

(a) Exactly what set of operations on derived views is supportable? As an example of this issue, imagine a request to delete a tuple from the SALES-CAT view described above. Since this view is a join of two underlying f i l e s , i t is not obvious what actions should be taken on the f i l e s to support the deletion. (Should we delete the SALES record but retain the CATALOG record?) For some kinds of view modification requests, there may be several possible actions which would produce the desired result; for other kinds of requests, there may be no possible supporting action. Codd [18] has described some examples of the l a t t e r phenomenon. (b) How should the view be bound to the available physical structures and access paths? This aspect of the binding problem concerns the optimization of the view and

144 accesses on scan, etc.

the

view in terms of available access paths, e.g., indexes~ sequential

(c) When should binding be performed?

For dynamic view d e f i n i t i o n , the binding must

also be dynamic.

In System R, we are investigating various binding-time

dynamic

w i l l occur for dynamically defined views but for certain often-used

binding

or very demanding views, the binding w i l l be done s t a t i c a l l y

with

strategies;

(hopefully)

an

increase in performance. 4.

Data Control F a c i l i t i e s

Data Control includes those aspects of a data base system which control the access to and

use

of data.

We distinguish four types of data control, each of which is being

investigated in System R. (a) Authorization.

This

form

almost a l l current systems.

of control is the most common type, being present in

Authorization is the mechanism to

permit

or

creation and manipulation of data structures and views by various users. System R may p o t e n t i a l l y be authorized selectively

grant

to

create

new tables

and

authorizations for his objects to other users.

deny the Any user of

views,

and

to

The authorization

mechanism of System R is described more f u l l y in [14]. (b) I n t e g r i t y .

I n t e g r i t y control provides a mechanism for enforcing that the data in

the data base obeys certain rules or predicates system.

which

have been declared

is l e f t to protocols imbedded in various application programs. types

of

control

facilities

are

provided:

integrity

I n t e g r i t y assertions are expressed in the SEQUEL language data

in

the

predicates. type

to

the

This form of control is t y p i c a l l y not found in current data base systems but

of

data

b a s e [15].

The

system

then

In System R, two main

assertions as

and triggers.

predicates

guarantees

the

Exactly when the system checks an assertion is a function

assertion

and

the

transaction

about

the

truth of these of

both

the

boundary which caused the assertion to be

checked. Triggers are actions that are invoked when some triggering detected.

For

example,

this

or

action

is

suppose that the DEPT r e l a t i o n contains an a t t r i b u t e NEMPS

which represents the number of employees in the department. of

condition

To maintain the v a l i d i t y

value~ we can declare triggers to update t h i s f i e l d whenever an employee is

hired, f i r e d , or transferred. (c) Consistency.

Integrity

implies

the

static

correctness

consistency is concerned with the dynamic correctness.

of the data base and

Suppose that one

application

program is t r a n s f e r r i n g a set of employees from Dept. 48 to Dept. 50, while simultaneously another application program is giving raises to a l l employees in Dept, 50. The interaction of these programs may have the undesirable r e s u l t that some but not a l l of the transferred employees receive the raise. E v e n worse, i f the transferring program encounters a f a i l u r e and backs out i t s updates, i t may develop

t45 that a raise has been given to In

current

systems

the

someone in Dept. 48.

application would contain specific statements (e.g., "LOCK

DEPT 50") to avoid these problems. defensive

A major goal of System R is

to

eliminate

coding which is not a part of the problem being solved but is related only

to the fact that the solution is running in a certain environment. cannot

know in

advance the

exact

environment

is

not

needed),

consistency. boundaries

the

system must

provide

The approach being pursued is to of

atomic unit. environment

Since

the

the

require

in

control that

this

case

user

define

the

a transaction, which is a sequence of statements to be executed as an The system then requests whatever resources i t needs

to

guaranteed

the

needed to enforce

the

guarantee

atomicity.

in

the

run-time

Furthermore, this same atomic unit is used as

the unit of i n t e g r i t y , i . e . , i n t e g r i t y may be suspended within a transaction is

user

in which his application w i l l run

(perhaps no other users are currently updating employee records; lock

such

at the transaction endpoints.

but

it

I f a transaction violates i n t e g r i t y at

i t s endpoint, then the transaction is backed out. (d) Recovery.

The fourth

aspect

of data control is concerned with preserving the

i n t e g r i t y of the data i f the system experiences a malfunction or backs

up either

voluntarily

if

an

application

or i n v o l u n t a r i l y , (e.g., as in the case of deadlock).

The recovery c a p a b i l i t i e s of System R include the usual checkpoint/restart as well

as

functions

the a b i l i t y to back up an ongoing transaction to user-specified points.

These c a p a b i l i t i e s are examples of functions which are required in order to

have an

operationally complete c a p a b i l i t y . ARCHITECTURE AND SYSTEM STRUCTURE We w i l l describe the overall architecture of Sytem R from two viewpoints. will

describe

description. a functional

the

system

as

seen by

Second, we w i l l investigate

a

single

i t s multi-user dimensions.

Figure 1 gives

programming language,

or

used to

directly

support various other interfaces.

The

Relational Storage Interface (RSI) is the access-method-like level which handles

the

access

a

we

view of the system including i t s major interfaces and components. The

RDI, as described previously, is the external interface which can be called from

First,

transaction, i . e . , a monolithic

to single tuples of base r e l a t i o n s .

This interface and i t s supporting system

(Relational Storage System - RSS) is actually a complete storage subsystem in that i t manages devices,

space

allocation,

storage buffers (one level s t o r e ) , transaction

consistency and locking, deadlock, backout, transaction recovery and Furthermore, i t maintains indexes on selected a t t r i b u t e s of base relations.

logging.

t46 r- -"i

!

r - --~

I ! !

I I I I I

t I

I

I

Relational Data Interface (RDI)

..JW

JO ::)

0

Z

H

W Z

~t W U

Z O H I---

Ul Z O ~ 1"~ U H

¢~

W gL

H

1E

Z O H

0

Z O ~-i I"-~t U lUL

ell

"~ Y Z~

gL E Z 0

Z L~ 2>

U~ I-Z W 2> w

_J

gl p..,~ H

Z o'1

~9

Z W

Z

H

O

Z

~J

o

E

2~

E

E

o~

o

x LU

o') LL

[RAS[

R[STOR[

SCRRTCHPRD

GET PRG[ 6 UZ(~ T I T L [ ~ ~ ' " SRU£ PAGE R(DRRU ......

Figure 11a. Data description function

]LIPUT RODE R ( P L R C [ ROD[ sON CH[CK RETURN

m

m

m

m

m

DELETE L I N E INSERT BLANK L Z N [ COPY L I N E DOWN PRZNT

DATA D [ | C R Z P T Z O N |

ZBfl [HPLOYEE F Z L [ ~ZTH X , Y [RP S2 HATCH KEY rROfl UNXflRTCH ~'14TCH CH 13 1 STR[[T HUR|[R r r N o ¢H S fLANK1CH t STRE[T NRH[ Sr~rN~R[ CH 16 9TRE[T TYP[ rTTYPE CH 2 NRfl£ L~TY CH 10 2"IP cz s COD( rLRNK2 CH 1 COORDZNAT£ CZ 7 rt.~NK3 CH 2 COORDZNRT[ TC! 6

C R [ R T [ AND [ D Z T

~0

=ON

ynJ;

X~t

;

S[L.[¢T][ONS

ERAS[

RESTORE

SCRATCHPAD

GET PAGE 15 VIEW TITLE~"-'-SAVE PAGE 15 REDRAM ........

~ 95125

TN[N

THEN

AND [ D I T

Figure 11b. Data selection function

I N P U T HODE R E P L A C E MODE CHECK RETURN

ZIP

OR Y ) l l l i J J 8 8 0

Y(=m

MNI[R[

OR X ~ 2 t t i a i l

XN Kz: M->Z

Intersection Relative complement Cardinality

Binary relation

{x[xeMiAx@M2}

operators

Ko: R-~R Rb: RxM-~R

Converse relation Restriction { (x,y) ~ (x,y)eRAxeM}

Rp: KxR->R RU: RxR-~R

Product Union

Reduction Vo:

{(x,y)~ 3 z:(x,z)eRIA(Z,Y)eR2}

of binar~ relations

R-~

Domain

{xI3y:(x,y)eR}

and a measure

191

Range

{xJ3y:(y,x)eR}

Na:

R-~M

Vg:

RxI-~M

Individual

domain

Ng:

RxI-~M

Individual

range

VgU:

RxM-~M

Restricted

domain

Reduction of measure Fw: FxI->D (n=2)

{xJ(x,I)eR} {x~(I,x)eR} {xl(x,y)eR^yeM}

functions

Logical 0Perators e: IXM-~B Test on set membership c:

In

MxM-~B

addition,

the

standard

Test on set inclusion

the standard arithmetic

logical operators and

comparison

are available

operators

as well as

for numbers

and

measures. Control m e c h a n i s m Sequencing

of operations

"Programs"

for the set theoretic machine

notation. Operations are performed nested argument, from inside out. Example:

A

question

such

would take the following c(Mw(Mcity),

are expressed

in a functional

from left to right and,

as "Are cities birthplaces

~or each

of engineers?"

form in the set theoretic machine

VgU(en(Rbirthplace),

Mw(Mengineer)))

Loops Loops are introduced three arguments:

by

resulting

the

use of bounded quanti£iers

i)

An expression

2)

An

3)

The name of a bound variable; invocation of the loop.

expression

(scope);

for

in a set of objects condition

it may be regarded

Important q u a n t i f i e r s are AL: MxB -~B all, every EI: MxB -~B some DB: MxB -~M

the

which

which nave

(range).

resulting

in

a

truth value

as the loop body. each o£ its substitutions

defines

an

192

ZB: Mx~ ->Z how many with the le£t-hane ~

the

set

bounding

and

the

le~t-nand

5 tne

conoition. Zxamples : DB

(x~Mw(~city) ~ e ( x r V g O ( e n ( R b i r t n p l a c e ) , M W ( M e n g i n e e r ) ) )

with the meaning DB

o£ "~nicn cities

are birthplaces

)

o£ engineers".

(x I , Mw (~manu f) ZB(x2, Vg(en(Rprod) ,Xl) , DB(x 3 , l~w (~lailment) , e(x2, Vg(en(Rmedic) ,x3)))))

with the meaning of "How many products m e d i c a t i o n s £or which ailments?" ~x~ressions Set

o£ which m a n u f a c t u r e r s

are

in the data base

membership

represen£ation

o£ an arbitrary o~

a

set,

~ind is expressed

arbitrary

set

Dy including,

expressions.

in the

Example

(in

German): Mrezeptp£1ichtig Ispasmocibalgin Vg(en(RDerivat), IOxazolidin)

®

IMorpnin Mw(MOpiate)

®

MW(MHypnotiKa) IMethadon Vg(en(RDerivat),

IS uccinimid)

Vg(en(RHeilmittel), where

~

indicates

drugs, Q a l l Tais

concept

all

opiates, is

its advantages are: - Since all objects

IAgitiertheit)

derivates

of

Oxazolidin

to be prescription

etc.

extended

to relations

and measure

functions.

are e v a l u a t e d on request only, changes

Dase may De made locally without that may exist.

Two of

to the data

regard to any interrelationships

193

- Expressions individuals

may be stored without regard for the existence of any for it. Hence one could construct a data base consisting

exclusively

of higher-order

One consequence, however, defined recursively since

relationships.

is that the control mechanism must itself be it may be invoked on any load operation.

3~3 Natu~@ 1 !anguage Few

users

will

feel

at

ease

with

the

highly

stylized

language

introduced in sec. 3.2. One possible step of abstraction, therefore, is the definition of a new abstract machine accepting natural language input. By necessity this is a highly restricted form of natural language

since

its semantics,

and hence

its syntactic

forms,

can be no

more than what may ultimately be reduced to a set theoretic interpretation. Moreover, it must be considered more restrictive than the set theoretic interface because while one may nest set theoretic expressions to an arbitrary depth, those beyond a certain depth simply cannot be stated To

speak

with

in n a t u r a l language

of

objects,

operators

natural

language

turns

in any comprehensible

and control mechanism

out

fashion.

in connection

to be highly unnatural,

It is possible,

that

in terms of the syntax of the interface which in turn may

level

however,

or rather

impossible.

to define an abstract machine

still be based on object types. This is in striking High High

similarity

on

to Very

Level languages vis-a-vis High Level program/r, ing languages: Very Level languages are loosely described as languages used to

specify what is to be done, rather

than how it is to be done

[SI 74].

In accordance with sec.2.2, the object types must relate to the ones of the set theoretic machine. In this case the relationship is straightforward as indicated by the following list: N proper names for the objects of the universe. A attributes (properties of an object of the universe). R references from one object of the universe to a second one Thebacon is referred to by Morphium M references to measures. D numbers or measures. S sentences.

These

or no, and proper names.

are of two kinds:

sentences

to

be

(e.g.

as its derivate).

sentences

answered

to be answered

by yes

by counting or enumerating

194

Some

examples

language

from

XAIfAS

in

which

German

was chosen as natural

interface.

Ist Psyquil

rezeptpflic__~ht_!~?

N A Betraegt die T a g e s d o s i s yon C n i n i d i n M

2 Gramm?

N

D

~elcne O e r i v a t e yon ~ o r p N i u m sina r e z e p t p i l i c h t i g f

The

syntax

of

the

inter£ace

is

describea

by

a 9ra~az

~itn tile

iollowing general properties: (i) S y n t a c t i c a l cannot

variables must

relate to the object types, hence

be based on tile traditional grammatical

noun,

noun

phrase,

essentially

adjective,

semantical

(attributes),

etc.

in nature.

RE(references),

categories

but on c a t e g o r i e s

they

SUCh as that are

The v a r i a b l e s are IN(names),

~F(references

to

measures),

ME ZA

(numbers) ~ SA (sentences), QO (quantifiers} . (2) On the other hand, the traditional c a t e g o r i e s inust be accounted for in some way, a consequence, features. sAS FE~ NED STR ATT ~OM

e.g.

in order

each syntactical

variable

incorrect

inflections.

is indexe~ my a number of

for

restricted

natural

nominative ) genitive ) case aative ) accusative ) wora c l a s s ( a a j e c t . / n o u n )

language,

grammars are Know~ to be

e x t r e m e l y complex because of the m u l t i t u a e of syntactic aspects be

observed~

insofar

As

Examples:

masculine ) NO~ feminine }gender GEN neuter ) OAT strong ~ e c l e n s i o n ACC attribute apposition ADJ number (singular/plural)

(3) gven

to reject

The

as it can be arranged

a) a c o n t e x t - f r e e grammar

in two levels,

in terms of the v a r i a b l e s

from (i); b) a feature program to be a s s o c i a t e d wit~l each p r o d u c t i o n on level a). Example:

Typical p r o d u c t i o n s of level a) are

aE

ME

-~

aE

ME - ~

RE

ME - ~ ~E -~

RE NE RE 1N

to

a p p l i c a t i o n of features s i m p l i f i e s tI~e grammar

SA -* ~IE sind ~h?

195

The production ME 1 -~ ME 2 ME 3 refers to the following feature program numbered

(syntactic variables are

for reference).

Part I: Test o~ right-hand features for acceptance (reduction takes place only i~ the condition is true). t__es~ (ME2,+ADJ+ATI')

A test

A ~!e~ (MAS,FEM,NE0,ME2,ME 3) A egu (NUM,ME2,~E3) Part 2: Assignment

(NO~,GEN,OAI,ACC,~IE2,~3)

of features to the syntactic variable on the

left-hand

side.

-ADJ-ATT,

co_~p (NUM,ME2),

and

(ME 3, -ADJ-Aq~) Ameq

(MAS,FEM,NEU,ME2,ME3) , a_qnd (NOM,GEN,DAT,ACC,~E2,ME3)

Feature operators are underlined. For example, test is true when the features of the first argument meet the condition specified by the second argument, me__qq is true whenever at least one of the listed features agree in both syntactic variables specilied, co~ copies the features ol the syntactic variable specified. 3.4 Pharmacolog~y The natural language level is supposed to serve a variety o£ application areas, we postulate that these application areas are all served

by

the

explainable only

in

the

in

same

natural

language

grammar

since

terms of set theory. Consequently, vocabulary

each ~ust De

these areas Giffer

they assign to the object types. Level 3 is

reached from level 2 simply by introducing names, and relating the object types. ~elow a few typical examples of assignment in the area of pharmacology. proper names

medications,

attributes

e.g. ~hebacon, Morphium, CIBA, Angina pectoris properties

references

e.g. Tablette, rezeptp~lichtig e.g. Indikation and Kontraindikation

references to measures

substances,

companies,

them to

are given

ailments,

(from ailment to

medication), Hersteller (from company to medication) e.g. Preis, Dosis, HaltbarKeit

numbers or measures

e.g. 5 DM, 2 %~abletten/i~ag, ~ ~oc~len

sentences

e.g. ~elche Preise haben Praeparate, die bei Angina Pectoris indiziert sind und deren Kont~aindiKation nicht Glaukom ist?

t96

3.5 T r a n s l a t i o n s ~he

path between aa3acent nodes

(3) and

(4)). ~e Shall briefly

natural t~ree

and

set

language.

traditional

code generation.

phases:

is traversed by translation

illustrate

(sec.2.3,

this for t~e passage between

In this case translation consists of t~e lexical

analysis,

syntactic analysis ano

The sentence

"~elche Firmen sind Herstelier

tablettenfoermiger

Medikamente?"

shall serve as an example. Lexical a n a l z s ! s Lexical

analysis

natural

language

exceptions,

includes the mapping level,

proceeds

and

for

from the p h a r m a c o l o g i c a l

each word encountered,

in three steps:

(i)

reduction of a word to its word stem;

(ii)

d i c t i o n a r y lookup resulting some

to the

with a few

of its features,

in a syntactical

variable,

and s m o r p h e m i c class,

level name for the word. (iii) a s s i g n m e n t of further

features

values of

as well as the set

on the basis of the m o r p h e m i c

class and the actual m o r p h e m i c ending.

• he lexical analysis of the entire word

Isyn.~

Ivar Welche Firmen sind Hersteller

Medika-

results

features

I

Q~ ME RE RE NE ME ME ME

tablettenfoermiger

sentence

in

]int.name

I +MAS+FEM+NEU -~OM+NOM+ACC FEM-NUM+NOM+GEN+DAT+ACC +MAS+NUM+NOM+DAT+ACC +MAS-NUM+NOM+GE~+ACC +MAS+NUM+NOM+AYT+STR+ADJ +FEM+NUM+GEN+DAT+ATT+STR+ADJ +f~AS+FEM+NEU-NUM+GEN+ATT+STR+ADJ +NEO-NUM+NOM+GEN+ACC

DB M26 R23

~9 M22

mente

Note the combinations lexical "Firmen',

syntactic ambiguities due to the d i f f e r e n t feature for "Hersteller" and "tablettenfoermiger'. Note also that

analysis all

by

four

itself cannot always determine cases are still possible),

"tabletten~oermiger') °

the case

(as for

or the gender

(as for

197

Syntactic

analzs!s

Syntactic analysis includes three phases: feature analysis (level b)), final code

reduction (level manipulation. For

a)), each

production applied, reduction and feature analysis follow each other immediately. Hence a production is applied in three steps: (i) Matching of input string and right-hand side. (ii) Test of right-hand features for acceptance. (iii) If true, reduction to left-hand side and assignment of features. For example, the production and feature program from sec.3.3 result in the following when applied to the phrase "tabiettenfoermiger Medikamente": ME2 ('tablettenfoermig'): I) +MAS+NOM+NOM+AT~+ADJ 2) +FEM+N~M+GEN+DAT+AT~+ADJ

(rejectea on m eq) (rejected on me_~q)

3) +MAS+FEM+NEO-NOM+GEN+AT~'+ADJ ME3 ('Medikamente') I) +NEH-NUM+NOM+GEN+ACC ME1 (result): i) +NEU+GEN-NOM-ADJ-ATT (note the disambiguation) The syntactic

analysis of the entire sentence

is illustrated

in figure

3. Because of the possibility of ambiguities the result is a parsing graph rather than a tree (in this case the ambiguity of the sentence is due to "Hersteiler'). The numbers adjacent to the syntactic variables refer to an associated list of features. Final code manipulation is left to the final stages of code generation, but must be considered part of the syntactic analysis because without it context-sensitive or transformational rules could not be avoided. ~o~e_g~neration Whenever a production is applied, a semantic action associated with it generates a functional set expression. Its arguments point to other such expressions unless they are individuals. Example: (tablettenfoermiger

Medikamente)

/ Mw (Mg) (tablettenfoermig)

MW (M221 (Medikament)

A,18

SA,19

~

M[,

14

ND HERSIELL

Figure 3

~\

Ip 9 RE, 8

ll

M£,I ~

ABL['r:[

-

~DIKAHEN

ME, 5 ME, ~ M~N [, N[o 2

?*. I

CO

199

WELCHE FIRHEN SIND HERSTELLER TABLZTTENFOERI41GER HEDIKAHENTE ?

02300047 I0000001 15000000 01100033 04000032 16000000 15000000 01100025 14100025 15000000 15000000 15000000 16000000 15000000 15000000 16000000 15000000 16000000 16000000 16000000 26000000

15000000 01100025 140000C5 15000000 16000000 01200001 10000001 15000000 01100045 01200040 01100C30 05000027 01200044 01100033 04000033 01100033 04000026 16000000 16000000 16000000 00000000

DB X1 t ~ M26 ) ( AA ~'T (22) ( ( ( ) ( ( ) ( ) ) ) E~IRBE

Figure 4

( AA ~'T ( 5 ) ( ) £ XI ( MV* VG* £N R23 MD ~H ~2Z MW H22 ) ) ) ........

200

On c o m p l e t i o n of the parse, syntactic

the pointer

variable SA is transformed

must be s u b m i t t e d

to a further

string m a n i p u l a t i o n

(i) C o m p l e t i o n of the syntactic

to the

This string

for two reasons.

analysis.

Quantifiers

do

not yet appear

them

is

subject

there

structure c o r r e s p o n a i n g

into a linear string.

to

a

in front of the expression.

~oving

number of rules that govern their

sequence. (2) O p t i m i z a t i o n . In many cases q u a n t i f i e r s can

The

cooe resulting

the p r i n t o u t Reverse Set

e.g. DB by

from translation o~ tne sentence adore is shown in

in figure

4.

translation

level names may

level

(whose e v a l u a t i o n may be time-consuming)

be replaced Oy stanaard set or relation operators,

immediately be translated

simply by again

conditions result.

(empty

invoking the dictionary.

sets)

into the p h a r m a c e u t i c a l However,

under certain

set e x p r e s s i o n s may themselves De part of a

This requires a translation

Examples: Vg(RI2, I14)

-~ Heiimittel

Mw(M9)

-~ t a b l e t t e n ~ o e r m i g

I2

-~ Verophen

into both level

2 and level

3.

fuer Psychosen

4 Semantic p_~rimitives as a basis 4.1 M o t i v a t i o n In

order

whether

to stuuy the a G e q u a c y of the rules o~ cn.2 anQ to d e t e r m i n e

they must be ~urther

of c o n s t r u c t i n g

systems,

refined or augmenteQ

to examine existing

in

t~e form of layers. One of the olQest

it

was

[Wo

not

conceived

that way)

it is help£ul,

systems of this ~ind

68,

~o

73]. Like the set theoretic approach,

of

objects

previous

approach,

is

taken.

but

the semantics data bases.

~oods"

universe

and i n t e r r e l a t i o n s h i p s between them. UnliKe

these are not c o l l e c t e d

treated as p r o p o s i t i o n s

This

(t~ougn

is Woods" q u e s t i o n - a n s w e r i n g machine

composed relations

snort

systems that are arrangeG

into m a t h e m a t i c a l

is the

sets and

to which a p r o c e d u r a l approach

is p r o b a b l y due to an o r i e n t a t i o n towards explaining of

natural language rather

than m a n i p u l a t i n g concrete

201

4.2 Semantic

Primitives

~bie~t_t~P~ O

Elementary

Fn

n-ary functions (n>l), e.g. departure x2). I~hese need not be functions function

objects,

may

yield

it is defined

Rn

e.g. Boston,

AA-57,

function

officer(x,O) = a 1 officer(x,al) = a 2

(end)

officer (X,an)

8:~0 a.m.

time (of flight x I for place in the strict sense. If a

more than one value

as a successor

(start)

(e.g. officer

of a ship)

such that

= E~D

n-ary

relation

arrive

(flight x I goes to place x2).

Designators

DC-9,

(predicate)

(n)l), e.g.

3et

(flight x I is a jet),

are either names of elementary objects or of ti~e form

Fn(Xl,...,xn) where x i is a (AA-57, Boston) for 8:00 a.m.

designator;

e.g.

departure

Propositions Rn(Xl,...,Xn) where x i is a designator; (AA-57), place (Boston), arrive (AA-57, Chicago). B

time

e.g. jet

Truth values

Example: (from

A

set of semantic

primitives

for the flight

schedules

[~o 68]):

Primitive

Predicates

CONNECT (Xl, X2, X3) DEPART (Xl, X2) ARRIVE

(XI, X2)

DAY (XI, X2, X3) IN (XI, X2) SERVCLASS (XI, X2) MEALSBRV

(XI,X2)

Flight X1 goes from place X2 to place X3 Flight X1 leaves place X2 Flight X1 goes to place X2 Flight X1 leaves place X2 on day X3 Airport X1 is in city X2 Flight X1 has service of class X2

JET (XI) DAY (XI) TIME (XI)

Flight X1 has type X2 meal service Flight X1 is a jet X1 is a day of the week (e.g.Monday) Xl is a time (e.g. 4:00 p.m.)

FLIGHT (Xl) AIRLINE (XI)

X1 is a flight (e.g. AA-57) X1 is an airline (e.g.American)

AIRPORT

X1 is an airport

(XI)

(e.g. JFK)

table

202

CIT~

(Xl)

Xl is a city

(e.g. Boston)

PLACE

(XI)

X1 is an airport or a city

PLANE

(XI)

X1 is a type of plane

CLASS

(XI)

X1 is a class of service

AND

S1 and $2

(SI, S2)

(e.g. DC-3) (e.g. £irst-class)

] |

Sl or $2 Sl is false

OR (Sl, S2) NO~ (Sl) IF~SE~ (Sl, s2)

~ |

(where S1 and $2 are propositions)

!

!

if Sl then S 2 J

Primitive F u n c t i o n s DTIME

(Xlo X2)

the d e p a r t u r e

ATIME

(XI, X2)

the arrival

NUMSTOPS

(XI,X2,X3)

time of Zlignt x1 from place X2

time of flight X1 in place X2

the number o£ stops of flight X1 between place X2 and place X3 the airline which o p e r a t e s flight X1

EQUIP FARE

(XI)

the type of plane of flight X1

(XI,X2,X3,X4)

the cost o£ an X3 type ticket from place X1 to place X2 with service of class X4

(e.g. the cost

o£ a one-way ticket from Boston to Chicago with first-class

service)

Qperators To

every

function

(procedure)

and relation there exists a p r o g r a m ~ e ~

which

subroutine

~ e t e r m i n e s a value of a £unction or the truth o£ a

proposition. Examples JET

(procedure names are capitalizeu) :

(AA-57)

-9

true

ARRIVE

(AA-57,Chicago)

-9

ARRIVE

(AA-57, boston)

-9

~alse

-9

8:~@ a.m.

D~II~

(AA-57, boston)

~nereas

the

specific terms

abstract

operators,

of

supplied

both by

the

microprograma~ing, adjusting

true

machine the

of cn.3 was Rased on object types Out

abstract machine

object and operator user

in this case

types. Specific

is define~

in

instances must be

for both of them. However, with the auvent of

computer

scientists

should have little p r o b l e m s

in

to this kind o£ notion.

Control m e c h a n i s m As

in

the

notation~

preceeing

e.g.

example,

p r o g r a m s are expresseo

in £unctional

203

TEST(CONNECT would

(AA-57, ~OSTON, C~ICAGO))

stand

for

"Does

AA-57 go £rom 5oston to Chicago?".

Likewise,

queries of any appreciable degree of complexity are based on the notion of bounded quantifier as a representative for loops. The £ormat for a quantified expression

is

FOR /:; where a type of quantifier (EACH,EVERY,SOME,THE,

nMANY).

a bound variable. class of objects over which quantification is to range. The specification is performed by special enumeration functions, e.g. SEQ,DATALINE,NUMBER,AVERAGE. Besides enumeration these functions may perform searches or computations.



restriction on the range

~ may both be quantified



scope

; expressions.

Unlike

KAIFAS

automatically

where the result of the evaluation of an expression retranslated

and

displayeG,

this

is

must be explicitly

requesteG by commands such as TEST (test trut~l o£ a proposition), PRINTOOT (print the representation for a ~esignator). Examples: (FOR EVERY X1 / (S£Q T~PECS):T;

(PRiNTOOT

(XI))

prints the sample numbers for all the lunar samples which are o£ type C rocks, i.e. breccias (T stands for "true"). (TEST (FOR 3~ MANY X1 / (SEQ FLIGHT):JET(XI); "Do 30 jet flights leave Boston?"

DEPART

(XI,~OSTON)))

4.3 Natural language As a general rule, the introductory remarks to sec.3.3 apply here as well: The level of the "English-like" query language provided on level 2 is influenced by t~%e range of expressions possible on the previously discussed

level i. In contrast to KAIFAS,

inspection of the data base

is not limited to the evaluation of level 1 expressions but may take place during translation from level 2 into level i, too. The semantic actions associated with a rule of grammar impose further restrictions, e.g. they make sure that the first argument of CONNEC~ is inaeed an instance of the class FLIGR~.

204

This

is

illustrated

syntactic analysis

by

the

£ollowing

example.

is p e r f o r m e d and a phrase marker

In a first step a is derived,

e.g.

NP

1 I M-57

NPR

/%

/\ 1

Since

verbs

in

~nglish

I~

,o

correspond

rougniy to p~eaicates, an~ noun

phrases are used to denote

the a r g u m e n t s of the predicate,

the

be

phrase

predicate. is

marker

will

In the example,

necessary

that

the

the

primary

factor

the p r e d i c a t e will be CONNECT.

subject

be

a

flight

the verb in

in d e t e r m i n i n g

and

that

the

For this it there

be

prepositional phrases whose objets are places representing origin (from) and d e s t i n a t i o n (to). The g r a m m a t i c a l relations among elements of a phrase marker

are defined by partial

GI:

S

/\ NP

G2;

S

t V 1 (2)

subjecl-verb

G3;

e.g.

S

i VP

VP

(I)

tree structures,

I t

VP

/ \ V 1

NP

i

{ I)

t2)

vetb-obj ect

/P\ PREP

NP

(| )

{ Z)

Pfeposffion-objec! modifying o VP

Among

the

phrase

three

n~arker,

structures,

v~hich of these

G1

and

G3 ootn match subt[ees

In the

is a c c e p t a b l e depends on the a~ditional

rules, e.g~ (GI:FLIGHT(1) ana(2) = fly). ((i) and (2) are p o s i t i o n a l v a r i a b l e s This rule o b v i o u s l y example,

the

is satisfied.

topmost

S-node

= to and PLACE((2))) ==>

tree structure).

More co~nplex rules are possible;

of the phrase marker

rule I-(GI:FLIGd%((1)) and (2) = fly) and 2-(G3: (i) = ~rom an~ PLACE ((2))) and 3-(G3:(I)

in the partial

CONNECT(I-I,2-2,3-2)

for

is matched by the

205

4.4 Air!ine 9 u i d e ~he system under discussion was first applied to a flignt seneQules table. TO illustrate the application interface, a few examples of queries shall be g i v e n below Does A m e r i c a n

(from

[Wo 68]).

Airlines

have

a

flight

departure

time

from

which

goes

from

~oston to

Chicago? ~hat is

the

Boston of every A m e r i c a n A i r l i n e s

flight that goes from Boston to Chicago? What A m e r i c a n

Airlines

flights

arrive

in Chicago from Boston before

1:8~ p.m.? Bow many

airlines

have

more

than

3 flights that go from Boston to

Chi=ago?

4.5 Lug~{ geology More

recently

the

system

evaluate the chemical that

accumulating

was

has

been

applied to access, compare ana

analysis data on lunar rock and soil composition as

a

result of the Apollo m i s s i o n s

[~o ?3].

Examples: What is the average c o n c e n t r a t i o n of aluminum in high alkali

rocks?

Give me all analyses of SI~046! How many breccias contain olivine? Do any samples have greater

than 13 percent aluminum?

What is the average model c o n c e n t r a t i o n of ilmenite

in type A rocks?

4~6 Critique (i) The

possibility

during

of

translation

confusion. related

Since,

to

inspecting the data base both on level 1 and from

definition,

reference

to

practical

repercussions:

necessitate control

the changes

mechanism

level 2 to level 1 introduces a note of

according data in

the

to sec.2.3, translation

base.

The

Either the

translation process

is d i r e c t l y

must

make no

lack of separation will have

certain changes on level 1 will

rules

of

grammar, or parts of the

for level 1 must be duplicated

for translation

purposes. (2) In

Wooas"

system

the

subroutines

their arguments are of the proper whether

AA-57

kind

do not appear to verlfy that (e.g. ARRIV~ Goes not c~eck

is indeed a flight or Chicago a place),

since this

206

is

done

on

translation~

then p r i m i t i v e These

interdependencies

the

parlance

corresponding arguments.

of to

relationships

this

those

structures unary

for

circumvents

predicates macnines

axioms

t~is

types that

or

must

restrict

accoun~ by

or

in

ranges

oi

machine

ana

not only

for

(~ote that

only

1

categories

of a D s t r a c t

as well.

problem

to level

to each oLner.

by a set oi axioms, Dy

tt~e c o n c e p t s

abstract

but

(correctly)

are related

may be e x p r e s s e d

data

between

terms

left

and functions

As a consequence,

primitive machine

If one

predicates

the KAl~AS

prescribing

all

operators.) (3) O p e r a t o r s albeit

(subroutines)

in

a

one-to-one

requirements are

met

governing

it

corresponding

5 Relational

ana

objects

fashion.

are

In order

the r e l a t i o n s h i p

suffices

to

procedure

as two

treat

interdependent to make

between

a predicate

instances

as well,

sure

that the

abstract

machines

or

function

o£ the same

and

its

resource.

model

5.1 M o t i v a t i o n One

oi

the

relational well

to

users

an

to

iormatte~

A

certain

reade r ' s

are

abstract

unlverse

same way:

field

names a

uniquely

a sequence

or,

as is

by

supposes

oi £ielGs are

ordered

a key,

i.e.

his

structures.

of entries

t~ey an

is Coua's

particularly

CoQ~

that may be named.

identified

Oases

itsel~

of table-liKe

ol a number

entry

is a relation

to Qata

lenas

machlnes.

in terms

the

formally,

a table

by

consists

or

More

consequently,

approaches

72, ~e 74] which

a table

exactly

headings

but

their

speaking, in

particular

alscusseQ

interpretation

attributes. named

widely

[Co 7G,Co

explain

Intuitively certain

most mooel

t~at

are

orGerea

called n-tuple ~ntries

on

here, and,

are not

the contents

ol

fields. familiarity part.

Only

with

the

relational

its i n t e r p r e t a t i o n

model

by a m a c h i n e

here.

5.2 R e l a t i o n a l

algebra

Qbie~t & A

attributes

Kn

relations

naming

a set of ob3ects

(domain)

is assumea

on the

will be e x a m i n e d

207

R n (AI,A2,...,A n) S A 1 x A 2 x ... x A n Example: S U P P L I £ R (SUPPLIERNR, ~AME, LOC), K E Y = S O P P L I E R N R SUPPLIER:

SUPPLIERNR

NAME

LOC

1

Jones

New York

2

Smith

Chicago

3

Connors

Boston

4

~hompson

New York

Key

attributes are indicatee;

anQ

other

Keys may be composite.

Hierarcnicai

relationships are usually eliminateo ~y normalization.

~ence all relations can be assumea to be normalizea. Tn

~

R n n-tuple.

Operators 9tand~d Rnl Q

[We 74] rela~ign o p e r a t o r s

Rn2 -9

Knl+n 2

Direct Product: {(Tnl~Tn2) JTnl E Rnl^Tn2 e R n 2 ) (~ C o n c a t e n a t i o n operator) } attributes

Rnu Rn

-~ R n

Union

R n ~ Rn Rn - Rn

-9 R n -~ R n

In t ~ r s e c t i o n l must be Di£~E~ence "compatible"

Special o p e r a t o r s Rn[A]

-9 R m

Projection:

Kelation R n restricteo

to the

attributes A={AI,...,Am}. Rnl [AQ~]Rn2-~ Rnl+n2Join: { ( T n l ~ T n 2 ) JTnl E Rnl ^ Tn2 ~ Rn2 ^ Tnl [A]~Tn2 where A,~ sets of attributes, @ one oi (Slight modifications, R n [A@B] -9 R n

Restriction:

e.g. natural

R n [A÷~]R n ->R m

~iv~sion:

[Co 71], p.74.

{=,9,,l}.

join, are possible).

{~nJTng R n ^ Tn[A]@Tn[B] }

where A,B,O as above.

[B]}

208

~o£tio ! ~e£h~n!s ~ Since are

all

operators

formed

by n e s t e ~

i~elational nave

by linear

For

5.3 R e l a t i o n a l

calculus

In

relation

place

oi

reduced

in

the

for

Individual

an e x a m p l e

algebra

see

Co~G

relational

infix

operators,

and

sec.

operands

"programs" rather

than

5.3.

proposes

an~

an a p p l i e u

proceeds

calculus

relation

constants,

constants,

Tuple

variables,

(attributes

to

show

preQicate tnat

(alpha-expression)

algebraic

may

any be

expression.

are

a I, a 2, a 3,

...

i,

.......

indexeu

2, 3, per

4,

relation

insteau

ot namee)

r I, r 2, r 3, ......

constants,

monodic, dyadic,

Logical

as

operators

the c a l c u l u s :

Index

Predicate

o£

calculus),

to an e q u i v a l e n t

Alphabet

defined

(ALPHA)

(relational

expression

been

sequences

expressions.

calculus

al~e£r~)

symbols,

PI,

P2,

P3, .... ;

=,~,,~

3, V , A , v ,

Delimiters. Simple

alpha-expressions

nave

(t I, t2, .... , tK) : w where - w a well-fo[meu -

formula,

terms

consisting

non-indexed

tuple

variable,

set

of

is p r e c i s e l y

tuple the

~xample:

Alpna-expresslon

suppliers

each

o£ W h O m

variables set of

indexeQ

occurring

in

free

ior

supplies

of an

variables

"~ino all

the

] P3r3((rl[l]=r311])

reduction

to r e l a t i o n

tl,

name

projects":

S1 = R1 S2 = R2 S3 = R3

s=sI®s2®

3

T 3 = S[I=6]~S~8=4~ T 2 = '1'3

[1,2,3,4,~]

TI = T2

[(4,5)÷(1,2)]S 2

A (r313]=r2[l]))

algebra:

or .o,

tk

in w.

r2{3]):

Plrl^~P2r2 After

form

t i distinct

- the

(rl[2],

t~e

and

location

oi all

209

= TI[2,3 ] ALPHA

is

a

appealing

language

to the user

may be r e f o r m u l a t e d I~ANGE S U P P L I E R RANGE

PROJECT

RANGE

SUPPLY

G~T ~

in A L P H A

SUPPLIER PROJECT SUPPLY

~or

((L.SUPPLIEk~=K.SUPPLI~R~k)

(order of q u a n t i f i e r s

similar do

of

tnis

to

= K.SUPPLIERNR) A (K.PROJNR

a

have

kind

is SQOARE

= P.PROJ~R)

each

such

statements

found

columns

However,

of a table

formal

looking the

been

shown

une

to oe

the view o£ [elatlens ~y ALPHA: for a value

one row after

examine

have

training,

wnich has been

from t~at offerea

to inspecting

value

of given

3 an~ 4 languages

[bo ?4]

calculus.

or columns

(as opposed

in cns.

to rely on a user's

is d i f f e r e n t

column

elements

to the ones

not

the relational

of values

SQUARE

A (~.PiO0~R=P.P~OONk))

be m a i n t a i n e d ! ) ,

L.LOC):

Dy SQOAR~

(ii)For

must

L

that

(i) Scan

as

levels

reducible offered

more

ine example

P ALL

reasons

language

is slightly

above,

L.LOC):

(L.SOPPLIERNR

devised

that

shown

K SOME

GET W (L.NAME,

5.4 Higher

form

K

or, e q u i v a l e n t l y

RANGE

expressions

F

(VP) (~K)

RANGE

alpha

L

(L.~AME,

RANGE

for

than the p r e d i c a t e

or a set

another).

corresponning

row anG

in this row.

are of a form suc~ as

("aisjunctive

mapping")

bRA(S) (read: is

a

"find B of R where A is S") relation,

respectively), Other

forms,

a similar

A S

e.g.

and is

an

B

that defines

a mapping

are sets of a t t r i b u t e s argument

for projection,

that may

conjunctive

itself

(domain

such

be an expression.

and n-ary mappings,

appearance.

Example : ~iA~iggMP DEPI' ( "TOY ") stanGs

for

"FinQ

the names

of e m p l o y e e s

that R

and range,

in ti~e toy aepartment".

nave

210

~ore a

recently

attempts

relational

[Co

~4].

ehs.3

data

%he a p p r o a c h

and

nave

base

4 in that

been

reporteo

that allow

system

in a ~ialog

~oun~eQ

~ii~ers

drastically

from

a truly

two-way

a user

to engage

on natural

~ngiisn

t~e ones ~ i s c u s s e o

communication

in

is envisioned.

5.5 Comment It

has

been

relational

shown

algebra,

expressible SQUARE

tnat botn

in

i.e.

ALPHA

are t h e m s e l v e s

any query

and

equivalent.

on tne s u c c e s s i o n

equivalence~

the

definition

~rom

the point

given

ss

relational

increasing notion

o~ user

level).

expressible

Equivalence

in relation

of the h i e r a r c h y ALPHA

indicates

does

- SQOAR£

that

ALPHA

is and

relation.

not preclude

by r e s t r i c t i o n

a hierarchy

to the

algebra

hence

is a s y m m e t r i c

machines

sophistication -

are e q u i v a l e n t

and vice versa,

of abstract

algebra This

of h i e r a r c n y

and SQUARh

in S Q U A ~ ,

The c o n d i t i o n does.

ALPHA

however still

De

(in the e i r e c t i o n

of

~urtner

coul~

refinement

on the

is necessary.

6 Conclusions There and

are

some

striking

similaritzes

between

the examples

o£ cns.3,4

5:

- In each - All

the lowest

rely

on

level

has been well

quantification

as

a

£ormalizeu. means

for

building

complex

expressions. -

All

- All

tend

towards

three

systems

On the other a

less

natural

hand,

formal

Experiences

have been only

but

indicate

~nile

a

objectives between has

been

that

successive

perhaps

in the belore.

translations, raise

o£

so far

system

Rave

(cn.3)

to provide level.

situations,

as well.

at the very

least

they

meet

the

languages

coulo

0£ course,

the r e l a t i o n s h i p

nigher

techniques

the e f f i c i e n c y

attempteo

to De made much more precise,

Furthermore, ane

some application.

on an i n t e r m e d i a t e

proof,

user

introduction. will

found

levels°

in some w e l l - d e f i n e d

do noc c o n s t i t u t e

levels

inoicateo

(ch.5)

the KAIFAS

higher

and

language

at least

nierarcnies

mentioned

o~ s u c c e s s i v e and

with

~ew e x a m p l e s

suggest

stylized

that,

on their

implemented

one of them

still

this may be n e c e s s a r y

Qo

language

of nigher

must

levels

imply

be e x p l o r e d

levels.

~inally,

did not attend to the critical q u e s t i o n what form take; this a p p e a r s to be a largely unsolved problem.

as

a number

to measure tne paper

the root should

211

Acknowiedgement~ The reading the manuscript

author is grateful to G.Goos and making helpful suggestions.

for carefully

Re£erences [Ab 74]

J.R.Abrial,

[BO 74]

R.F.~oyce, D.D.Chamberlin, W.F.King, M.M.Hammer, Specifying Queries as Relational Expressions, in [KI 74], 169-176

[Bu 72]

Burroughs Corp., Language (ESPOL),

[Co 76]

E.F.Codd, A Relational Model for Large Snared Data BanKs, Comm.ACM 13(197~), No.6, 377-387

red 72]

E.F.Coad, Relational Completeness of Data base Sublanguages, in: ~.Rustin (ed), Data Base Systems, Courant Computer

Data Semantics,

in [KI 74], 1-59

B6700/77~ Information

Science Symp.,

Executive System Programming Manual, 1972

Prentice-Hall,

Inc. 1972, 65-98

red 74]

E.F.Coea, Seven Steps to Rendezvous in [KI 74], 179-199

with the Casual 0ser,

[Col 68]

L.S.Coles, An Online Question-Answering System with Natural Language and Pictorial Input, Proc. 23rd Natl. ACM Conf. (1968), 1.69-181

[Go 73]

G.Goos, ~ierarchies, in F.L.Bauer (ed), Advancea Course on Software Engineering, Lecture Notes in Econ. and Math. Systems, vol.81, 29-46

|Gr 69]

C.C.Green, The Question-Answering Univ. 1969

[~i 74]

J.W.Klimoie, Nortn-Hollana

|Kr 75]

K.D.Kraegelo~, P.C.Loc~emann, Bierarcnies o£ Data Languages: An Example, Information Systems (in print)

[Su 74]

B.Sundgren, Conceptual Foundation of Approach to Data Bases, in |KI 74], 61-94

[SI 74]

ACM SIGPLAN Symposium on Very High Level Languages, 1974, ACM, New York 1974

Application o£ ~neorem Proving to Systems, Tech. Rep. ~o. CS138, Stanford

K.L.Koffeman (eds), Publ. Co. 1974

Data

Base

the

Management,

Base

In£ological

March

212

[i~e 74]

H.WedeKino, Data Base Systems I, ~I-~issenscna£tsverlag~ Reine Informatik, vol.16, 1974 (in German)

[Hi 68]

N.Wirth0 Computers,

PL3~6, A Programming Language Journ.ACM 15(1968), No.l, 37-74

[wo 68]

~.A.WOOdS~ Machine, 457-471

Proce0ural Semantics £or a Question-Answering Proc. AFIPS Fall Joint Coff!p.ton~l 33(1966),

[No 73]

WoA.~oo~s~ Progress in Natural Application to Lunar Geology, 42(1973)~ 441-450

£or

tne

36~

Language 0nde[stan~lng - An Proc. AFIPS ~ati.Comp.uon£.

Ein System zur interaktiven Bearbeitung umfangreicher Me~daten Ulrich Schauer,

IBM Deutschland GmbH, Wiss. Zentrum Heidelberg

Zusammenfassung Bei der Bearbeitung von Megdaten mu~ man unterscheiden zwischen einer Standardauswertung der Messungen, bei der eine bestimmte Modellvorstellung zugrunde liegt und einer Analyse mit dem Ziel, logische Zusammenhange zu erkennen und ein erkl~rendes Modell zu finden. W~hrend die Standardauswertung durchaus im Stapelbetrieb ablaufen kann mit einem Datenmodell,

das abgestimmt ist auf die im Modell ablesbaren Verknfipfungs-

m6glichkeiten,

ist ffir die Analyse ein interaktives System

wfinschens-

wert mit einem Datenmodell, das beliebige Verknfipfungen erm6glicht und mit einer Datenmanipulationssprache,

die mSglichst deskriptiv sein soll-

re, aber komplexe Auswahlkriterien erlaubt. Verf~gbare Systeme werden den Anforderungen der Analyse nur teilweise gerecht, meist mangelt es der Datenmanipulationssprache

an F~higkeiten zur rechnerischen Datenbe-

arbeitung. Im folgenden wird ein experimentelles System ffir die Bearbeitung von Megdaten beschrieben,

an dem im Wissenschaftlichen Zentrum der IBM in Hei-

delberg gearbeitet wird.

t.

EINFOHRUNG

Umfangreiche Sammlungen yon Megdaten k6nnen erst in vollem Mage nutzbar gemacht werden, wenn die f~r die Analyse zust~ndigen Fachleute Wissenschaftler,

(z. B.

Techniker - meist ohne groge Programmiererfahrung)

in die Lage versetzt werden, ohne Zuhilfenahme von Programmierern selbst die Bearbeitung vorzunehmen. Dazu ist ein interaktives System erforderlich, das erlaubt, Teilmengen der Daten unter komplexen Auswahlkriterien zu bilden und in vorhandene oder neu zu schreibende Bearbeitungsprogramme zu stecken und die Ergebnisse tabellarisch oder graphisch darzustellen.

214

Schon bei den Auswahlkriterien k6nnen recht verwickelte Berechnungen anfallen, die z w e c k m ~ i g

mit Bausteinen aus einer Programmbibliothek

durchgeffihrt werdeno Anpassung des Systems an bestimmte Fachgebiete ist damit m6glich durch Anpassung der zugrundeliegenden Programmbibliothek. Da nur eine begrenzte Anzahl yon vorgefertigten Programmen zur Verffigung stehen kann~ wird h~ufig noch Datenmanipulation durch eine Tr~gersprache (host language) notwendig sein. Als Tr~gersprache ist APL ffir die angestrebte Zielsetzung besonders geeignet durch ein hohes Mag an Interaktivit~% durch Anpassungsf~higkeit

an die Programmiererfahrung des Ben~tzers

und eine Vielzahl yon Operationen zur Datenmanipulation. Figur ! vermittelt einen 0berblick fiber den Systemaufbau. DatenManagementsystem

........ IInformationsSystem

DatenManipulations System

Interaktive Tr~gersprache

(APE) FIGUR I:

System-Aufbau

Die Datenbank enth~it sowohl Problemdaten als auch beschreibende Dateno Programmbibliothek steht symbolisch f~r eine Sammlung von Programmen, die in PL/I, FORTRAN oder Assembler geschrieben sein k6nnen und die von APL aus mit Daten aus dem APL-Arbeitsspeicher oder der Datenbank angestogen werden k~nnen und ihre Ergebnisse wieder im APL-Arbeitsspeicher abliefern. Die Benfitzer-Kommunikation erfolgt mit APL oder mit einem der in APL eingebetteten Systeme zur Manipulation yon Megdaten, Pro-

215

grammen und zugeh~riger Dokumentation. Als Benftzerstation

(Terminal)

kommen in erster Linie Bildschirm und Schreibmaschine in Frage. Einen 0berblick fiber die Datenkomponenten,

die vom System zu verwalten

sind, gibt Figur 2. Katalogbearbeitung beschreibende Daten

ProblemDaten

5) und zur Datenmanipulation

(z. B.

x ÷ y ÷ z-tOO) ffir numerische und abgesehen yon arithmetischen

216

Operationen auch ffir nicht numerische Daten. Die Verwendung yon APL als Tr~gersprache erlaubt insbesondere auch bequeme Manipulation yon Rechtecksstrukturen yon numerischen und yon Textdaten (Vektoren~ Matrizen). b) Unterprogramme

zur L6sung von standardisierten Problemen aus Ge-

bieten wie Mathematik tiation) und Statistik

(z. B. numerische Integration und Differen(z. B. lineare Regression, Testverfahren,

Darstellung yon H~ufigkeitsverteilungen c) Anwendungsbezogene zeichnungen,

Standardverfahren

etc.).

(z. B. Analyse von EKG-Auf-

Klassifizierung von FingerabdrQcken etc.).

Die Tr~gersprache APL mit einer Vielzahl von verf~gbaren APL-Bibliotheksprogrammen und der M 6 g l i c h k e i ~ v o n

APL aus

graphische Darstellungen zu

initiieren, bietet schon alle M6glichkeiten zur Datenmanipulation.

Trotz-

dem sind die Klassen b) und c) notwendige Bestandteile des Systems. Die Klasse b) erlaubt Ausweichen auf FORTRAN, PL/I oder Assembler geschriebene Unterprogramme,

was besonders bei grogen Datenmengen bessere Rechen-

zeiten bringen kann. Programme der Klasse c) existieren vorwiegend in FORTRAN oder PL/I~ weil sie meistens f@r Anwendung im Stapelbereich entwickelt werden. 2.2

Problemdaten

Das System ben@tzt ein relationales Datenmodell~ die Datenbank besteht aus einer Sammlung umfangreicher Tabellen, die mit leicht verst~ndlichen Operationen manipuliert werden k~nnen (Codd /1,2,3/). Datenattribute sind den Spalten einer Tabelle fest zugeordnet wie beim SEQUEL-System (Boyce, Chamberlin /4,5/). Spezifikation von Teilmengen von Daten aus einer oder mehreren Tabellen erfolgt mit einer an Beispieleintragungen in die fraglichen Tabellen orientierten deskriptiven Sprache, die sich gleichermagen fur den Einbau von Unterprogrammaufrufen ablauf eignet

in den Programm-

(Zloof /6/).

Die Datenelemente in einer Tabellenspalte k~nnen dimensionierte Daten sein (z. B. Vektoren, die eine Me~reihe darstellen oder Matrizen, die mehrere Megreihen oder eine Funktion yon zwei Ver~nderlichen darstellen k6nnen etc.)° Die offensichtliche Mehrdeutigkeit wird duTch eine der Tabellenspalte zugeordnete Interpretierung behoben. a) Interpretierungsattribut: Regelt die Deutung einer Matrix, z.B. als Werte einer Funktion yon zwei Ver~nderlichen in den Punkten eines gleichabst~ndigen Gitters. Die Definition der Gitterpunkte

217

(x ° + i.h, Yo + j'k)

i = O, I, ..., m-1 j = O, I, ..., n-1

erfolgt durch Angabe von Xo, Yo' h, k und m, n. b) Darstellungsattribut: Erlaubt Spezifikation yon Verdichtungsmechanismen fur Datendarstellungen in Erg~nzung zu beispielsweise I, 2, 4 byte integer. c) Speicherungsattribut: Die meisten Daten werden in der XRM-Datesbank gespeichert digitalisierte

(Lorie /7/). Umfangreiche Datenelemente

(z. B.

Bilder) k6nnen jedoch auch in yon CMS (Conversational

Monitor System) verwalteten Band- oder Platten-Dateien

abgelegt

und in XRM nur durch Angabe ihres Dateinamens und einer Zugriffsroutine bekannt gemacht werden. Das System besorgt automatische Umwandlung physikalischer Einheiten und automatische Datenkonversion entsprechend Interpretierungs-, Darstellungsund Speicherungsattribut

sowie Beachtung yon durch logische Bedingungen

definierten Konsistenzregeln

bei neuen Eintragungen

oder ~nderungen in

einer Tabelle. 2.3

Beschreibende

Daten

Das System zur Manipulation der unformatierten

Kataloginformation

ist

eine selbst~ndige Komponente mit F~higkeiten fNr Generierung, Wartung und f@r rechnerunterstNtztes Auffinden der relevanten Katalogeintragungen Nber Daten und Algorithmen (Erbe, Walch /8/). Formatierte Datenbeschreibung wird in der XRM-Datenbank

gespeichert und umfaSt jeweils ein

Verzeichnis von: a) Umwandlungstabellen

f~r physikalische

Einheiten.

b) Methoden mit Programmidentifikation. c) Datenattributen mit Tabellen und Spaltenbezeichnern. Mittels b) und c) kSnnen Programme und Tabellen rasch identifiziert werden, wenn die Bezeichnung der Methode bzw. der Attribute der fraglichen Tabellenspalte bekannt sind.

3.

DIE DATENMANI~ULATIONSSPRACHE

Zun~chst sind zwei Sprachebenen vorgesehen.

218 Prgz!durale Sprachebene

3.1

Die folgenden Eigenschaften

kennzeichnen die prozedurale Datenmanipula-

tion: a) Der Datenzugriff erfolgt durch APL-Befehle (Lorie, Symonds /9/)° b) Umwandlungen zwischen der externen Datendarstellung in der XRMDatenbank und der internen Datendarstellung (z. B. Darstel!ung und Speicherung). c) Konsistenzregeln

erfolgen automatisch

werden automatisch kontrolliert bei Datenzug~ngen

oder Ver~nderungen. d) Die Daten werden tabellenweise e) Der Ben~tzer ist verantwortlich ten hinsichtlich physikalischer

oder zeilenweise verarbeitet. fur korrektes Verarbeiten der DaEinheiten und Interpretation.

Deskriptive SpFacheben ~

3.2

Die nicht prozedurale

Sprache EQBE stellt eine Erweiterung dar von QBE

(Query by Example, Zloof /6/). Sie eignet sich auch fur Ben~tzer mit geringen Kenntnissen in APL (Erfahrung im Umgang mit APL als Tischrechner gen@gt) und ohne Programmiererfahrung. Die Sprache ist in hohem Ma~e deskriptiv. Relationen und in der Programmbibliothek verf~gbare Unterprogramme werden als Tabellen dargestellt, und der Ben~tzer formuliert seine Datenauswahl, indem er entsprechende Zeileneintragungen vornimmt, die Ausgabewerte bezeichnet und Auswahlkriterien - soweit erforderlich durch APL-Statements definiert. EQBE l ~ t sich am besten anhand yon Beispielen erkl~ren. 3.3

Beispiele R

~

r

Ix

zu E~BE ist ein Schema fur eine Tabelle mit dem Namen R und

I y ~

zwei Spalten mit den Bezeichnern RI und R2.

Die Werte x~ y stellen eine Tabellenzeile

dar, r ist ein Bezeichner

diese Zeile. r, x, y werden vom Ben~tzer eingetragen in das Schema

R

IRI

fur

I R2 ~ I

a

das vom System geliefert wird, wenn man Tabelle R anfordert. Die Datenvariablen x, y k6nnen alle in R gespeicherten Tupelwerte annehmen.

{ ( x , y) I

(x, y)

!. Auswah! einer Spalte

O÷ X

e R}

(Projektion)

219

Die Angabe

eines

Zeilenbezeichners

ist als Symbol Die Abfrage Gesucht

ffir Ausgabe

ist nicht notwendig.

zu verstehen.

lautet:

ist die Menge

Eine m6gliche

der x Werte

Formulierung

{x I ~ ( x ,

y)

Selbstverst~ndlich nur auf Werte Im folgenden

aus RI.

im Pr~dikatenkalkfil

wgre

ER}

erstreckt

sich der Definitionsbereich

aus der R2-Spalte schreiben

von y

yon R.

wir daffir auch k~rzer

{x I u ( x , ) } und fassen u(x,) in R existiert, 2. Einfache

als Pr~dikat dessen

Abfrage

gersprache R

RI

R2

u

x

y

mit einschrgnkenden

formuliert

x>

auf, das wahr

erste Komponente

ist, wenn ein Tupel

gleich x ist.

Bedingungen,

die in der Trg-

werden.

,31 z

5 +yxy

(z < 25) V (z > 50)

D~x {x [~3u(x,y,z) yz

A (x > 5 + y × y)

A ((z < 25) V z > 5O) }

3. Schnittmenge

x > y z = 10 ~÷x T r g g t man i n S a n s t a t t das APL-Statement

z den konstanten

W e r t 10 e i n ,

z = 10.

oder {x ]~/9 r ( x , y ) yz

A s(x,z)

A (x > y )

A (z=lO)}

so e n t f ~ t l l t

220 4. Vereinigungsmenge

x1> y z = 10 0÷

x

{ x

] ~y u C x , y , ) A Cx> y) } L) { x

} 3z

vCx,,z)

(x

i (#. u(x,y,) A (x> y)) v ::]zzvCx,,z) A (z=1O)}

A (z=10)}

oder

S.

Differenzmenge

r

x

y

D+x {x

[ ~ r(x,y) A ~ s(,x) }

Selbstverst~ndlich muB jede Datenvariable, die in einer negierten Tupelvariable auftritt, auch in einer nicht negierten Tupelvariabfen auftreten (oder als globale Variable bekannt sein). 6. Kartesisches Produkt

R RI I

x ...... :I

r

O+

x,y,xl,z { (x,y,xl,z) I r(x,y) A s(xl,z) }

7. Equijoin (Restriktion im Kartesischen Produkt)

-

~1 ~

~ I1~1 Ix i"I ~'2"'I ~

~+x,y,z { (x,y,z) I r(x,y) A s(x,z)}

221

8. Verallgemeinerter

Join mit nachfolgender

R

RI

R2

S

$I

$2

r

x

y

s

xl

z

Projektion

x_>y B÷z

{z

I 3x x-3I -3y

r(x,y) A S(Xl,Z) A (x >- y)}

Anstelle des _> Operators k~nnte eine beliebige goolsche Funktion stehen. 9. Division R r

RI Ix

R2 I y

I

S

$I

$2

T

TI

T2

s

x

z

t

.y

z

~]+x {x I~z ¥Y6 r r(x,y)A s(x,z) A t(y,z)} .y steht fiir {y l~x ~z r(x,y) A wobei

-4

s(x,z)}

,

bedeuten soll, daI~ x fest zu w~hlen ist, und das Auf-

X

treten yon .y in t ist so zu verstehen,

dab gilt ~ Y6.Y

t(y,z)

10. Gruppierung

{x Iv v { r ( x , y ) A s(x,z)A t(y,z)} g

kann bis jetzt noch nicht formuliert werden. Man braucht ein Hilfsmittel, um AbhRngigkeit zwischen Variablen anzugeben. Mit der Vereinbarung,

daf~ y.z bedeuten soll-I ~z ' sind die entspreY chenden Eintragungen :

sis ] r

x

y

s

t

..............

.y

y.

zl

D+x Wir sind jetzt in der Lage, jede Operation der Relationenalgebra auszuf{ihren. Die Vollst~ndigkeit yon QBE in der vorgestellten erweiterten Form ist damit fiir einfache Abfragen, die nur eine Operation der Relationenalgebra

umfassen,

erwiesen.

Sie folgt auch fur beliebig zusammengesetzte Operationen: Jede Abfrage yon QBE etabliert bei ihrer Definition eine logische Datensicht, die der Resultattabelle entspricht. Erst bei Ausf~hrung eines APL-Programmes) das yon einem Abfrageprozessor

aus der logischen Datensicht erzeugt wird,

222

entsteht die Resultattabelleo

Eine neue Abfrage kann auf der iogischen

Datensicht yon schon definierten Abfragen aufgebaut werden, und damit kann eine komplexe Abfrage in Einzelschritte aufgel~st werden. 3.4

Diskussion der Erweiterungen von QBE

Die nachfolgend beschriebenen Erweiterungen erlauben die Behandlung yon recht komplexen Abfragen, wie sie bei Me~daten zu erwarten sind, ohne die Einfachheit f~r elementare Abfragen zu beeintr~chtigen. a) In einer Programmbibliothek erfa~te Algorithmen (APL-Funktionen, FORTRAN-Unterprogramme, PL/1-Prozeduren oder Assemblerroutinen) k6nnen f~r Datenauswertung oder Datenselektion innerhalb einer Abfrage eingesetzt werden. b) Beliebige APL-Befehle k6nnen innerhalb einer Abfrage zur Datenselektion und Auswertung verwendet werden. QBE erlaubt auger den Vergleichsoperationen nur eine begrenzte Anzahl eingebauter Funktionen wie COUNT, SUN etc. ¢) Die Resultattabelle einer Abfrage kann durch Angabe yon formatbeschreibenden Formularen auf verschiedenste Art dargestellt werden, auch in graphischer Form und wiederholt mit wechselnden Formularen. d) Dutch jede Abfrage wird eine logische Datensicht definiert, die zur Entkoppelung komplexer Abfragen in einer Folge von einfacheten Abfragen verwendet werden kann. e) Jede Abfrage kann zu wiederholten Malen ausgef~hrt werden. Dabei k~nnen von Mal zu Mal die Werte globaler Variablen ge~ndert werden. F@r APL-erfahrene Ben~tzer er6ffnen sich dadurch interessante Mgglichkeiten zur Datenbearbeitung mit anpassungsfghigen Bausteinen. f) Der Entkopplungseffekt von QBE, da~ die Zeileneintragungen in beliebiger Reihenfolge m6glich sind, wurde noch verst~rkt (Verwendung der Gruppierungsm6glichkeit). g) Durch die Gruppierungsm~glichkeit k~nnen auch Abfragen ohne Zerlegung in aufeinanderfolgende Schritte bearbeitet werden, die sich der Behandlung durch QBE entziehen. h) Als Gegenst@ck des ALL D-Operators (all different) von QBE dient in EQBE ein vorgesetzter Punkt, entsprechend beim ALL-Operator (alle mit Wiederholungen) ein vorgesetzter Punkt und Angabe des Tupelbezeichners in Klammern gesetzt. Eine Pseudovariable wie .y oder .x (r) kann in APL-Befehlen verwendet werden und steht stellvertretend ffir einen Bereich gleichartiger Werte.

223

4.

MESSDATENBEARBEITUNG

4.1

Das Datenbearbeitungssystem

APL ist zur interaktiven Analyse von Me~daten, die im APL-Arbeitsspeicher Platz finden, hervorragend geeignet (Schatzoff /10/). Bei gro~em Datenumfang verliert APL an Attraktivitgt, weil Datenselektion aus Tabellen dann aus Platzgr~nden nicht im APL-Stil durch eine Operation abet einen dimensionierten Bereich dargestellt werden kann, sondern nur durch eine Rekursionsvorschrift ~ber alle Tabellenzeilen. Eine prozedurale Sprachebene mit APL als Trggersprache

ist daher noch nicht voll zufriedenstel-

lend. Ein weiterer Gesichtspunkt bei Me~daten ist, da~ Messung h~ufig f@r die Zusammenfassung

von vielen Einzelwerten

steht (z. B. digitalisierte

Me~-

kurve). FUr die Bearbeitung solcher Messungen ist es w@nschenwert yon der Tr~gersprache APL aus, Programme, die in einer anderen Sprache (FORTRAN, PL/I, Assembler) Andere experimentelle

entwickelt wurden, aufrufen zu k6nnen.

Datenbanksysteme,

die APL als Trggersprache

ver-

wenden, sind meist nur ffir geringen Datenumfang konzipiert (Palermo /I]/), Klebanoff, Lochovsky, Tsichritzis /12/) und erlauben den Einsatz von Programmen,

die nicht in APL geschrieben wurden, entweder gar nicht

oder nur mit ineffizienter Datenkommunikation

(~ber externe Dateien).

Bei der in Figur 5 beschriebenen Architektur erhalten wir ein System zur Probleml~sung mit Datenbankzugriff

auf zwei Sprachebenen

(prozedural und deskriptiv)

Einsatzm~glichkeit von vorgefertigten Programmen aus einer leicht erweiterbaren Programmbibliothek (FORTRAN~ PL/] oder Assemblerprogramme) Hilfsmitteln Programme

zur Verwaltung der Dokumentation fiber Daten und

Automatischer Datenumwandlung in gew~nschte physikalische Einheiten Automatischer Datenkonversi~n, soweit durch Implementierung, Darstellung und Speicherung erforderlich Unterstfitzung graphischer Ein/Ausgabegergte Verffigbarkeit von Programmen zur graphischen Darstellung - einer Schnittstelle

f~r leichte Substitution von Ein/Ausgabeger~ten

224

VM /370 Conversational Monitor System

I CP/CMS o~andos ~ Informationssystem (Daten,Methoden)

i Nicht procedurale Sprachebene (EQBE)

Procedurale Sprachebene (DB-Service) Dateizugriff Spooling

XRM DB-System ProgrammBib lio thek (FORTRAN, Assembler, PL/I)

Schnittstelle ~ilfs'~ f@r prozessoren , Ein/Ausgabeger~te

Menutechnik etc.

Station

FIGUR 3: Systemarchitektur

]

Biid-~ schirm I

I

~a~in

225

4.2

Be , i s p i e l e

zur D a t e n b e a r b e i t u n $

Die folgenden zwei Beispiele sollen die Fghigkeiten zur Probleml~sung illustrieren.

Im ersten Beispiel wird die Verbindung mit Programmen aus

einer Programmbibliothek gezeigt, im zweiten Beispiel unter anderem die Bengtzung von globalen Variablen. 1. Welches in der Datenbank erfaBte Material hat einen mittleren Reflexionsbeiwert

.~TERIAL~PEKTREN

(zwischen 250 und 300 nm) gr6ger als 60?

~{¢TERIALNAME

REFLEXIONSSPEKTRUM

material

reflexion

AUSGABE

SIMPSONREGEL

INTEGRALWERT

integral

xl

÷

250

x2

÷

300

STARTWERT 150 NM

SCItRITTWEITE 5 NM

,,EINGABE iNTEGRAND

150

GRENZEN

reflexion

xl

x2

60 gamma[KG-DN~3]xc[CAL.GRADxG] xlambda [CAL.CMxGRADxSEC] Bei dieser Formulierung ist die Existenz einer Eintragung in der Tabelle }~9\TERIALWERTE gesichert. Eine widersprechende Eintragung k6nnte augerdem existieren (falls t~NTERIALNAME nicht Schlfisseleigenschaft hat). Bei der folgenden Abgnderung ist entweder die zusfitzliche Bedingumg erffillt oder nicht entscheidbar Eintragung der Materialwerte

MATERIALWERTE

(weil keine

existiert):

SPEZ. ~ I E ' .... IMATERIALW)~RME 1 LEITF)~HIGKEIT INAME

i GEWICHT [gamma' '

c

]

lambda

[material

0.5 being definable as ~ x ~ , t x , y ~ )

(the ordered pair

and one can say that x bears

the relationship R to y provided that <x,y> g R ("~" being the predicate of set membership).

Thus, the confusion between a

"relation" and a "relationship", terminological

idiocy,

which is another example of

is made quite precise.

379

Relations of interest can be given names and defined either by enumeration of their members or by any property that must be possessed by a pair to enjoy membership,

in exactly the same fashion

that any other set is completely defined by its members. The equally troublesome concept of "order" can be explicitly defined.

A partial ordering is any relation having the properties

of reflexivity,

anti-symmetry and transitivity.

A linear ordering

is a partial ordering where any two elements in its field are comparable and a well-ordering is a nowhere dense linear ordering. Structures of arbitrary complexity can be constructed. of a general array

The concept

(Steel 1964) developed out of some early data

structure studies, and it can be shown that any nondense complex is expressible as a general array so defined.

As digital computers

cannot deal with dense structures except in finite approximation, this would seem to be sufficient. The modal predicate of deontic logic, its derived predicates "-0-"

"O-"

"O"

(for "obliged to"), and

("obliged to not" E "forbidden to"), and

("not forbidden to" I "permitted to") provide the required

paradigm for expressing either legal constraints in the model or defining the rules of access.

These examples could be multiplied a considerable length, but should be sufficient to illustrate the point.

From a theoretical point of

view there is no more suitable vehicle for expressing a conceptual schema.

This is, of course, not the whole story.

First, theoretical possibility and practical possibility are not identical.

There is the danger that the necessary expressions get

too large and cumbersome for effective use. with million instruction operating systems,

In an age where we deal this is not a fully

380

persuasive argument in any event.

It is, however, moot.

The number

and character of the necessary expressions do not get excessive; unlike,

say, the contrast between conventional procedure languages

and Turing machines.

On the contrary, nearly a century of search

for compact notation has resulted in definitional sequences that provide more compact expression than one typically finds in programming language data descriptions perform less of the task.

(or sub-schemas)

which

Some of this is due, of course, to the

use of large character sets, but in any case economy of notation is not a problem. A second potential difficulty is the actual use of the tools to construct the desired models, which is a task that is necessarily an art rather than a science.

Clearly,

if the process of constructing

a model could be itself formalized one would already have the model in the input.

To this point I can only say that I have personally

been partially successful

in constructing models of relatively

complex insurance procedures,

and in a matter of a few days,

inventing notation as I went along.

This effort was only partially

successful in the sense that, while I was able to generate static models with no difficulty,

the problem with time and the dynamic

behavior of the model caused difficulties of two types.

First,

thre was the philosophical problem of the potential as opposed to the actual.

How does one treat the property "age at death" prior to

the actual death of the individual?

Formally,

of course, this is

trivialF but obtaining some assurance that the formalism does not hide an ambiguity or paradox is far from trivial. The second problem with time has to do with the inelegance of making the variable denoting time distinguished and, therefore, case.

a special

While there is nothing inherently wrong with mathematical

381

inelegance per s e, several thousand years of logical and mathematical history suggest intuitively that something is wrong. Some recent work

(Thomasen 1974) on the reduction of tense logic to

modal logic hints at a solution to this problem. I have gone far enough with this work to become convinced that the approach is sound and no fundamental invention is required; some hard work to refine the ideas.

There remains,

only

however, one

further potential criticism of this approach with which it is necessary to deal.

It is a criticism to which I would prefer to

comment "a pox on those who raise it" and then ignore the matter. As a practical consideration,

however,

it will not go away.

It is

much the same argument that has been raised in the past against every programming language except COBOL;

i.e., the language is too

much like algebra, only the mathematicians can use it.

The argument

is irrefutable for if people believe they cannot understand something,

they won't!

However,

there is one difference between

this situation and the programming language situation.

The only one

who must construct models is the enterprise administrator and only the data base administrator and the applications administrators need to read such models. well compensated.

These individuals are presumably senior and

They can be required to have a little education.

Furthermore, while I have no proof, it is my belief that once the barrier of belief in its esoteric character is overcome,

it is no

harder to teach reasonably intelligent people the relevant logic than it is to teach them COBOL and the DDL.

To summarize this personal view of the nature of a conceptual schema, any alternative is either equivalent and therefore equally complex while being less understood for lack of familiarity,

or it

is not equivalent and therefore can only model a subset of that

382

reality otherwise amenable to modelling.

The only real issue is

whether some less powerful but more acceptable formalism exists that is adequate for modelling anticipated enterprises for a reasonable future.

In my view neither data structure diagrams

nor normalized relations

(Bachman 1969)

(Codd 1970) nor the CODASYL DDL

(CODASYL

1971) being discussed at this Working Conference are candidates for such an alternative.

As overlaid structures for internal and

external schemas they may be quite suitable;

the criteria for

acceptability being different. In conclusion~

let me reiterate that the latter portion of this

paper is my personal view of the appropriate structure for a conceptual

schema and does not necessarily represent the view of

other members of the ANSI/SPARC Study Group on Data Base Management Systems.

On the other hand, the general principle of the three

level approach and the essential requirement for the conceptual schema is fundamental to the deliberations of the Study Group.

It

is reasonable to claim that this position will be maintained in the Final Report of the Study Group and will continue to characterize the official position taken by ANSI on behalf of the USA in any deliberations on data base management systems in the ISO.

383

REFERENCES Bachman,

C. W.: "Data Structure Diagrams",

Bernays,

P. and Fraenkel,

Data Base,

A. A.: "Axiomatic

Set Theory",

North-Holland Braithwaite,

R. B.: "Scientific Press

Carnap,

R.: "Introduction (Cambridge,

CMSAG Joint Utilities

1:2 (1969).

Explanation",

(Amsterdam

1958).

Cambridge University

(London 1953). to Semantics",

~

Harvard University Press

1942).

Project:

"Date Management Requirements",

Systems CMSAG

(Orlando,

FL

1971). CODASYL:

"A Survey of Generalized available

CODASYL":

from NTIS

(Washington,

Banks",

CACM,

13:6

(1970), pp. 377-387.

Mathematica

und verwandter

(1931), pp.

173-198.

"Data Base Management Inc.

Hilbert,

(New York,

D. and Ackermann,

W.:

669.

Systems

I", Monatshefte,

SHARE

N. Y. 1970). "Grundzuge der Theoretischen

1938). (Geneva-3)

S~tze der Principia

System Requirements",

Logik",

ISO: ISO/TC97

(New York 1971).

Model of Data for Large Shared Data

K.: "Uber formal unentscheidbare

GUIDE/SHARE:

Systems",

DC 1969).

"Data Base Task Group Report", ACM

Codd, E. F.: "A Relational

G~del,

Data Base Management

Julius Springer

(Berlin,

38

384 Kleene,

S. C.:

"Introduction (Princeton,

Quine, W. V. 0.:

SPARC:

"Outline for Preparation

"Interim Report: Systems:,

Steel,

T. B.; Jr.:

A.:

for Standardization", DC 1974).

Study Committee on Data Base Management

"Beginnings

(forthcoming).

of a Theory of Information CACM,

7:2

(1964), pp. 97-103.

Begriffe der Methodogie

Wissenschaften

und

S. K.: "Reduction of tense logic to modal logic,

I",

37

I", Monatshefte

der deduktiven

f~r Mathematik

Physikt Thomason#

Harvard University

MA 1961).

(Washington,

SIGMOD NEWSLETTER

"Fundmentale

rev.ed.,

of Proposals

CBEMA

Handling", Tarski,

Logic",

(Cambridge,

document SPARC/90,

van Nostrand

N. J. 1952).

"Mathematical Press

SPARC:

to Metamathematics",

(1930), pp. 361-404.

J. Symbolic Logic, Von Wright,

39:3

(1974), pp. 549-551.

G. H. : "An Essay in Modal Logic", (Amsterdam

1951) .

North-Holland

385

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