Table Of ContentLecture Notes in Artificial Intelligence 116
Subseries of Lecture Notes in Computer Science
Edited by J. Siekmann
Lecture Notes in Computer Science
Edited by G. Goos and J. Hartmanis
M. E Papazoglou J. Zeleznikow (Eds.)
The Next Generation of
Information Systems:
From Data to Knowledge
A Selection of Papers Presented at Two
IJCAI-91 Workshops, Sydney, Australia
August 26, 1991
galreV-regnirpS
Berlin Heidelberg NewYork
London Paris Tokyo
Hong Kong Barcelona
Budapest
Series Editor
J6rg Siekmann
University of Saarland
German Research Center for Artificial Intelligence (DFKI)
Stuhlsatzenhausweg 3, W-6600 SaarbriJcken ,11 FRG
Volume Editors
Michael .P Papazoglou
Queensland University of Technology, School of Information Systems
GPO Box 2434, Brisbane, QLD 4001, Australia
John Zeleznikow
La Trobe University, Database Research Lab., Applied Computing Research Inst.
Bundoora, VIC 3083, Australia
CR Subject Classification ( 1991): 1.2.4, 11.2.1
ISBN 3-540-55616-8 Springer-Verlag Berlin Heidelberg New York
ISBN 0-387-55616-8 Springer-Verlag New York Berlin Heidelberg
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Preface
Traditional database systems have been able to manipulate large amounts of data
efficiently, whilst artificial intelligence (and in particular expert systems) have reasoned
with rules, but rarely with data. It has become evident that to build truly intelligent
information systems, we require facilities from artificial intelligence, database and
distributed technologies.
IJCAI-91 was held in Sydney in August 1991. It is the major international forum for
academics, researchers and practitioners in the discipline of Artificial Intelligence.
Independently of each other, two workshops focusing on building intelligent
information systems were organised: one on Integrating Artificial Intelligence and
Databases, the other on Intelligent and Cooperating Information Systems. The
organisers of the workshops recognised that their sessions had much in common, and
decided to coordinate their work. Whilst the workshops were run individually, a joint
program was organised. Seventy papers were submitted and thoroughly refereed.
Twenty of these papers were presented in the workshops. The authors of seventeen of
the papers were asked to rewrite their papers for this book.
This book hence consists of seventeen contemporary research articles on constructing
intelligent information systems. It has utilised the talents of over one hundred
researchers in the area. Special thanks are due to the people listed on the following
page.
We would also like to thank those people who submitted papers and attended the
workshop. This book would not have been completed without the untiring efforts of
Alfred Hofmann of Springer-Verlag.
This book is a tribute to the authors of the articles. We take full responsibility for any
errors.
May 1992 Mike Papazoglou
Queensland University of Technology
John Zeleznikow
La Trobe University
IV
Executive Support
Patrick Bobbie
Mike Brodie
John Hughes
Patrick Valduriez
List of Reviewers
R. Beichorazani (University of Ulster),
P. Bernus (University of Queensland),
P.O. Bobbie (University of Western Florida),
M.L. Brodie (GTE Laboratories, Waltham, Massachusetts),
R. Colomb (University of Queensland),
H.H. Dai (University of Ulster),
J.G. Hughes (University of Ulster),
M. Huhns (MCC, Texas),
D. Karagiannis (University of Ulm),
S. Keronen (University of Ttibingen),
E. Knudsen (Cap Gemini, Sweden),
D. Ling (university of Ulster),
L. Marinos (Erasmus University of Rotterdam),
M.P. Papazoglou (Queensland University of Technology),
H.W. Schmidt (ICSI Berkeley),
J.L. Smith (CSIRO, Australia),
J.J.P. Tsai (university of Illinois at Chicago),
P. Valduriez (INRIA - Rocquencourt, France),
G. Williams (Australian National University),
N. Yoshida (Kyushu University, Japan),
I. Young (University of Ulster),
J. Zeleznikow (La Trobe University)
Table of Contents
Introduction
The Next Generation of Information Systems -
From Intelligence to Distribution and Cooperation
M~. Papazoglou, J. Zeleznikow
Part 1: Intelligence
A Data and Operation Model for Advanced Database Systems . . . . . 9
S. Danforth, E. Simon
An Object-Oriented Data Model to Represent Uncertainty in
Coupled Artificial Intelligence-Database Systems. 37
R. George, B.P. Buckles, F.E. Petry
Common Architectures for Databases and Knowledge-Based Systems 49
I.E. Jelly, J.P. Gray
The Construction of Maintainable Knowledge Bases. 58
J. Debenham
Adding Qualitative Reasoning to an Organizational Database for
Management Decision Support . . . . . . . . 79
H.S. Yuen, S. Ho, J. Zeleznikow
Building Human-Centred Intelligent Cooperative
Information Systems with IKEA . 401
M. Stolze, M. Gutknecht, R. Pfeifer
Database Organization for Qualitative Analysis: The NUDIST System 611
T. Richards, L. Richards
Using a Prolog Engine to Integrate Multiple Knowledge Sources:
The KCM/Help-Desk Project . . . . . . . . . . . 431
R. Bland, J. Cowie, T. Kane, C. Rattray, I. Wilson
From Relations to Objects: A Translation Methodology
for an Object-Oriented Front-End to RDBMSs 841
L. Marinos, R.A. Smit
IIIV
Part 2: Distribution and Cooperation
A Framework for Cooperative Adaptable Information Systems . 169
J. Vittal, B. Silver, W. Frawley, G. lba, T. Fawcett, S. Dusseault, Ji Doleac
Problem Solving in Federative Environments:
The FRESCO Concept of Cooperative Agents 185
S. Kirn, A. Scherer, G. Schlageter
Heterogeneous Database Integration Architecture
Based on a Conversation Theoretic Skeleton . . . . . . . . . . 204
P. Bernus
Coarse-Grained Distributed Agents for Transparent Access
to Remote Systems . . . . . . . . . . . . . . 223
S.C. Laufmann
A Forward-Chaining Information Framework . . . . . . . . 238
J.C. Weber
Using Negotiation and Coordination in Multi-Agent Intelligent
Cooperative Information Systems . . . . . . . . . 251
K.J. Werhnan
A Distributed Cooperative Agents Architecture for Software Development 271
J.J.-P. Tsai, R.-Y. Sheu
Knowledge Selection in Large Knowledge Bases 291
D. Karagiannis, F.J. Kurfefl, H.-W. Schmidt
The Next Generation of Information Systems -
From Intelligence to Distribution and Cooperation
M.P.Papazoglou J. Zeleznikow
Queensland Univ. of Technology La Trobe University
School of Information Systems Database Research Laboratory
GP O Box 2434 Applied Computing Research Institute
Brisbane Queensland 1004 Bundoora, Victoria 3083
Australia Australia
The next generation of computerized information systems will rely on the
ability to store, access and reason about large volumes of information (with a
possible natural spatial distribution). This paper identifies approaches,
principles and research directions intended to support advanced features of
the next generation information systems which will be characterized by refined
'intelligence' and various forms of cooperation.
This paper serves as an overview to the two parts of this book, which deals
with several aspects of intelligence, distribution and cooperation ni the realm
of modern information systems. Accordingly, the first section of the paper
examines the notion of intelligence and its connection with information
systems, and identifies technical requirements regarding intelligent
information systems. The second section argues that most computerized
information systems ni the 1990s will be distributed and tasks will be most
likely performed by a pool of intelligent information systems acting
autonomously, cooperatively or collaboratively depending on the complexity
of the tasks and the resources required to complete a corporate task. The third
and final section of the paper summarizes the chapters within each of the two
parts of the book and briefly describes various forms of intelligence,
distribution and cooperation proposed by the authors of these chapters.
1. Intelligence
The topic of knowledge based and intelligent information systems is a very
broad one. nI this paper we limit ourselves to considering expert systems
which focus on rule-based knowledge representation technology using facts,
rules, and either forward or backward chaining. Such systems have been
employed successfully for solving a variety of complex problems which
require human expertise. An ever increasing demand for the deployment and
effective use of expert systems is observed in areas which include automated
office applications, CAD/CAM, VLSI design, medical diagnosis systems, and
military command and control. Applications ni these areas are particularly
demanding as regards knowledge-directed processing of a progressively
increasing body of shared information. However, the majority of expert system
applications is restricted to limited data sets and have no facility for performing
sophisticated data management activities.
As we start progressively introducing expert systems into engineering
traditional computer environments and applications, we must rely on
advanced data management capabilities to support the emerging tools. When
database technology is appropriately combined with expert systems it offers
the potential for allowing knowledge-bases to be shared amongst several
applications. tI also provides facilities for manipulating persistent data as well
as persistent knowledge [1], [2], [3]. However, the problem of interfacing these
two technologies is quite acute. Although, the evolution of the database and
knowledge-based technologies seems to converge, the main problem is that
these two technologies are at present quite distinct. Conventional database
technology has been conceived to support transaction-intensive data
processing applications and has made its primary concern the ability to store,
maintain and access large amounts of data representing facts organized in
well-formulated structures. nI contrast, knowledge-based technology, such as
frames, has focused on an increased expressiveness, or the ability to
represent many different and complex types of data - at all granularity levels -
and their relationships with increasing depth and precision. This kind of
advanced functionality can be conveniently employed to support knowledge
processing applications and tasks such as diagnosis, design, configuration,
scheduling, resource allocation, planning and interpretation.
Database systems are evolving in the direction of capturing and expressing
more semantics in their conceptual schemas, while expert systems are trying
to deal with applications that require an increasing amount of facts to be
cached to complement their general rules. The interfacing of expert system
and database technologies should lead to systems capable of managing a
large body of complex knowledge in an integrated way. The deep and
significant commonality between knowledge bases and databases stems from
their fundamental concerns about knowledge and inference: =they are both
first and foremost information-bearing systems" [4]. It is only natural that
interaction or merging is considered at the knowledge-level, i.e., conceptual
level, whereby a database can be viewed as a simple kind of knowledge-
base, equipped with an elementary form of knowledge representation and
providing rudimentary inference capabilities, and is put under the direct
auspices of the specific knowledge representation scheme employed. This
may ultimately involve the development of a unified knowledgeldata model
capable of expressing both data and knowledge semantics.
It is clear that we are heading towards a new breed of information systems
(IS). An IS is a conglomerate of applications that implement required
functions, over a collection of shared persistent data and (possibly)
knowledge, which represents a cross-fertilization of concepts from AI,
knowledge-based and data management systems. The purpose of an IS is to
fulfil the demands of a human client. The appropriate combination of the
database and expert system technologies leads to the emerging concept of an
intelligent information functionality. Such systems rely upon both the general
problem solving capabilities of expert systems and the data handling
capabilities of advanced database management systems.
As the concept of an intelligent information system has diverse connotations,
we shall define it as an information system which combines knowledge-
based and database primitives and concepts to support applications that
require =knowledge directed processing of shared information = [1]. To achieve
the greatest possible leverage from available knowledge and data-based
resources, it is not adequate to simply couple a database management
system (DBMS) to an expert system and use it as a back-end, the expert
system and the DBMS must be designed to operate in harmony: each of them
must be perceived as the natural extension of its counterpart in a completely
unified system.
From the perspective of developing "intelligent" information systems, this
particular definition has to be examined along the lines of the degree of
integration among the expert system and the DBMS components of an
intelligent information system. This implies examining two possible scenarios:
- Enhancing the DBMS with more expressive structuring and manipulation
tools that take more application domain semantics into account.
- Allowing the expert system to access and to efficiently handle a large
body of shared information stored in a data repository(ies).
2. Intelligence, Distribution and Cooperation
Most modern enterprises and organizations rely on many application-specific,
stand-alone information systems which they have developed over a long
period of time to satisfy their needs. Although these systems fulfil the bulk of
their requirements, enterprises have gradually began to appreciate the
potential and implications of acquiring information from distant but related
information sources - that may span multiple sites and organizations - in a
manner that guarantees that business is to be transacted in a uniform way.
This requires developing corporate information systems that deal with a
complex range of problems which can be solved by a synergy of ISs working
together in a cooperative manner. For example, consider the vast numbers of
different ISs that need to interact to support the functions of a bank, or an
insurance company with multiple branches and subsidiaries, a hospital using
external specialists, therapists and laboratories, or even an application
involving epidemological inquiries based on correlation of statistical data
relating to animal or plant disease outbreaks over large agricultural regions.
As a rule, the discrete pieces of network-resident information become a more
valuable resource when integrated, allowing them to shed light into relatively
"obscure" folds of a single problem which can be dealt with in its entirety only
by pulling together pre-existing but related information sources. This corporate
resource can be brought to bear in solving a range of complex information-
intensive problems which can not be solved by any of the existing individual
ISs in the network acting in isolation. Stated from the user stand-point, there is
a pressing concern to access and distill an ever growing wealth of remotely
stored information - managed by a number of qualitatively different in-
formation systems - in an efficient, transparent and user friendly manner.
All of the aforementioned factors have contributed immensely to the
emergence of distributed information technology which attempts to collectively
manage large volumes of corporate-wide information to lower production and
maintenance costs, so as to obtain the best possible leverage from pre-
existing information-intensive applications and resources (e.g., processing,
