johnz@latcs1.lat.oz.au (John Zeleznikow) (05/24/91)
ADVANCED COURSES AT THE DATABASE RESEARCH LABORATORY, APPLIED COMPUTING
RESEARCH INSTITUTE, LA TROBE UNIVERSITY, VICTORIA AUSTRALIA
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Please find enclosed information on advanced courses on
. distributed and object oriented databases
. intelligent information modelling
. artificial intelligence and the law.
The fee for each course is $950 ($475 for academic staff and $95 for students
and the unemployed), and includes all use of software and hardware, written
materials and meals and refreshments. Two courses will cost $1,500 and all
three courses will cost $2000 (with appropriate reductions for academics,
students and the unemployed.
For further information and registration forms contact:
Dr. John Zeleznikow
Database Research Laboratory
Applied Computing Research Institute
La Trobe University
Bundoora Victoria 3083 Australia
Phone: 61.3.4791003
FAX: 61.3.4704915
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DISTRIBUTED AND OBJECT - ORIENTED DATABASE SYSTEMS COURSE/WORKSHOP
Lecturer: Dr Patrick Valduriez - Inria, Rocquencourt France
Venue: La Trobe University
This course will involve designing and constructing both distributed and
object-oriented database systems - technologies for the twenty first century.
The course will consist of lectures, tutorials, laboratories and supervised
practical work. Participants will have the opportunity to use the new object
oriented database management system O2, developed at INRIA.
Dr. Valduriez is currently the director of the Sabre Database Project at INRIA.
He is the co-author of renowned books on distributed databases, and relational
databases and knowledge bases. He gave a tutorial at VLDB90 on distributed and
parallel databases.
Dr. Valduriez will be supported by Dr. John Zeleznikow and his team at the
Database Research Laboratory at La Trobe University. Dr. Zeleznikow is an
associate editor of the Australian Computer Journal, Tutorial Chairman of
VLDB90, and General Chairman of an IFIP WG2.6 Conference on Interoperable
Databases.
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COURSE OUTLINE
DISTRIBUTED AND OBJECT-ORIENTED DATABASE SYSTEMS
Dr. Patrick Valduriez, INRIA, Le Chesnay, france
DISTRIBUTED DATABASES
CENTRALIZED VERSUS DISTRIBUTED DATA MANAGEMENT
Critical features of relational databases: data independence, query
optimization, reduction of data redundancy, transaction support.
Additional objectives of distributed databases: distributed computing,
site autonomy, increased reliability, extensibility and performance.
Approaches to distributed data management.
DISTRIBUTED DBMS ARCHITECTURES
Transparencies: network, location,location, transparency.
Architectural models: ISO/OSI, ANSI/SPARC, client-server model.
Impact of standards: ISO SQL, IBM's SAA, ISO RDA, ISO TP.
Global directory management issues.
DESIGNING A DISTRIBUTED DATABASE
Alternative design strategies: top-down vs. bottom-up design process.
Distributed database design issues.
Designing the best fragmentation: horizontal, vertical or hybrid.
Selecting the best allocation of fragments to sites.
PROCESSING AND OPTIMIZING QUERIES ON A DISTRIBUTED DATABASE
Distributed query processing: problem, objectives, layers.
Localization of distributed data: reduction rules for fragmentation.
Optimization of distributed queries: cost model, database profiles,
join ordering in fragment queries, algorithms.
CONCURRENCY CONTROL AND RELIABILITY IN DISTRIBUTED DATABASE SYSTEMS
Transaction management concepts: atomicity, consistency, isolation,
durability.
Distributed concurrency control: serializability theory, algorithms,
distributed deadlock management.
Distributed reliability: distributed protocols (two-phase commit, three-
phase commit, network partitioning).
INTEROPERABILITY USING DISTRIBUTED MULTIDATABASE SYSTEMS
Integration of heterogeneous data models: schema integration.
Multidatabase definition and manipulation within SQL.
Multidatabase transaction management issues.
MDBS developements: DB2 V2, INGRES/STAR, ORACLE/STAR, SABRINA*, SYBASE.
IMPROVING PERFORMANCE AND RELIABILITY WITH PARALLEL DATABASE SERVERS
Data Servers: objectives, data servers in distributed databases.
Multiprocessor architectures: message-passing versus shared-memory.
Parallel data placement: load balancing and replication issues.
Parallel query processing.
Parallel data servers: NONSTOP-SQL (Tandem), DBC (Teradata), BUBBA (MCC),
GAMMA (U. of Wisconsin), GRACE (U. of Tokyo), EDS (ESPRIT).
OBJECT-ORIENTED DATABASE SYSTEMS
EVOLUTION OF DBMS REQUIREMENTS
New application domains (OIS, CASE, CAD/CAM,AI, etc.).
History of DBMS: theoretical and practical advances.
Persitency and programming languages.
Strenghts and weaknesses of Network and Relational DBMS.
Objectives of OODBMS: richer data types, complex objects, computing power.
THE OBJECT-ORIENTED APPROACH
Objectives and history.
OO concepts: object, class, method, message, inheritance, polymorphism.
Advantages: encapsulation, modelling, modularity, code reusability.
OO programming languages: C++, Smalltalk, Simula, Eiffel.
Strenghts and weaknesses of OO.
THE OODB APPROACH
OODB concepts: persistence, sharing, identity, collections.
Procedural vs. declarative programming: the impedance mismatch.
OODB problems: persistence model, encapsulation, objects and values, class
extensions, compilation and optimization, data control, schema evolution,
transactions.
OODB DATA MODELS
Evolution of data models to capture more semantics.
Extending the relational model with OO capabilities: INGRES OBJECT, ESQL.
Extending an OO model with DB capabilities: ONTOS, GEMSTONE.
Creating a new data model: ORION, O2.
OODB LANGUAGES
Extending SQL with abstract data types, object identity, complex objects:
ESQL, OSQL.
ISO extensions to SQL: SQL2, SQL3.
Extending an OOPL: ONTOS with C++, OPAL with Smalltalk, O++.
Extending a PL: ORION with LISP, O2-C and O2-C++.
Query languages for OODB: the Object Management Group.
IMPLEMENTING AN OODBMS
Integration of OO and DB capabilities: difficulties.
The three architectural approaches:
extending an RDBMS: POSTGRES, SABRINA.
extending an OO system: ONTOS, GEMSTONE, OBJECT-STORE, OBJECT-BASE.
creating a new system: ORION, O2, GBASE, IRIS.
Architecturing OODBMS in workstation/server environments.
Implementation issues: object and memory management, versions, indexes,
transactions, set-oriented operators.
ANALYSIS AND COMPARISON OF OODBMS PRODUCTS
Common description outline: objectives, model and language, architecture,
implementation techniques, additional features.
Analysis and comparison of POSTGRES, SABRINA, GEMSTONE, ONTOS, ORION, O2.
TOWARDS HETEROGENEOUS, DISTRIBUTED OODB MANAGEMENT
The toolkit approach to extensible object management: EXODUS (U. Wisconsin),
GEODE (INRIA), ARJUNA (U. New Castle).
Distributed object management (DOM): common object protocol, local
application interfaces and distributed objects managers.
Heterogeneous information systems integration: the DOM breadboard (GTE
Labs.), the FUGUE project (Xerox).
Issues: security, semantic integrity, physical integrity.
DATES: 9a.m-5p.m. July 29,30 and 31, August 1 and 2 1991.
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INTELLIGENT INFORMATION MODELLING COURSE/WORKSHOP
Lecturer: Professor Robert Meersman, Tilburg University, Netherlands
Venue: La Trobe University
This course will involve designing and constructing modern information systems.
It will include Entity-Relationship. NIAM and Binary Relation Models. There will
be an extensive discussion of object-oriented databases and intelligent
information systems, as well as CASE tools.
The course will consist of lectures, tutorials, laboratories and supervised
practical work.
Professor Meersman is the director of INFOLAB, Tilburg University Netherlands.
He is chairman of IFIP WG2.6 (on databases) and is currently writing a book on
Advanced Information Modelling Techniques.
Professor Meersman will be supported by Dr. John Zeleznikow and his team at the
Database Research Laboratory at La Trobe University. Dr. Zeleznikow is an
associate editor of the Australian Computer Journal, Tutorial Chairman of
VLDB90, and General Chairman of an IFIP WG2.6 Conference on Interoperable
Databases.
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SYLLABUS
Day 1. Introduction to information modelling.
Pitfalls of normalisation.
Entity Relationship Model.
Semantic Data Modelling.
Day 2. Principles and notation of NIAM (Nijssen Information Analysis
Method)
Binary Modelling.
Examples of NIAM and Binary Modelling.
Comparing NIAM and Binary Modelling to Entity Relationship Model.
Day 3. Mapping NIAM Conceptual Models to Implementation Models e.g. SQL
Day 4. CASE (Computer Aided Software Engineering) tools based on NIAM
and Extended Entity Relationship Modelling.
Day 5. Object Oriented Data Modelling
Replacing NIAM concewptual modelling with object oriented
modelling.
Illustration and case studies.
DATES: 9a.m. - 2p.m. Mondays 1,8,15,22, 29 July 1991.
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ARTIFICIAL INTELLIGENCE AND LAW - COURSE/WORKSHOP
Lecturer: Professor Donald H Berman
Northeastern University, Boston, Massachusetts
Venue: La Trobe University
This course will involve designing and constructing legal information systems
that provide intelligent advice. No prior knowledge is assumed.
The course will consist of lectures, tutorials, laboratories and supervised
practical work.
Professor Berman is currently Richardson Professor of Law and the director of
the Center for the Study of Law and Computer Science at Northeastern University
in Boston Massachusetts. He is editor of the Journal "Artificial Intelligence
and Law" and has been a key-note speaker at the International Conference on
Artificial Intelligence and Law.
Professor Berman will be supported by Dr. John Zeleznikow and Mr. George Vossos
who have developed the IKBALS II Prototye. IKBALS II adds reasoning with
precents into an object-oriented rule-based system which advises on the
likelihood of successful claims under the Accident Compensation (Workcare) Act.
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COURSE OUTLINE
Seminar on Artificial Intelligence and Law
Professor Donald H. Berman
Richardson Professor of Law
Northeastern University School of Law
Co-director Northeastern University Center for Law & Computer
Science
July, 1991
I. INTRODUCTION
This course investigates two questions: 1) what is the impact of
artificial intelligence (AI) on the legal profession and the legal
system? 2) what is "legal reasoning" and how can it be represented in
a form understandable by a computer.
For computer science students interested in AI this course will
provide insight and experience in modeling judgmental reasoning
in a complex domain. For those interested in law this course enhances
traditional lawyering skills by analyzing the lawyering process
through the magnifying lens of AI. For both lawyers and computer
scientists this course will offer the opportunity to explore methods
for the more efficient delivery of legal services while providing
the intellectual challenge of trying to develop formal models that
explain how the legal systems works both in theory and practice.
II. SUBJECTS COVERED
A. A Historical and Conceptual Overview of AI & LAW.
B. Basic Knowledge Representation.
1. Statutory Normalization - removing syntactic ambiguity
from legal documents by use of logical formalisms.
2. Production Rules - The use of logical formalisms to
represent legal rules.
3. The potential and limitations of logical models of legal
rules.
C. Working with cases I -
1. Extracting deep structure rules from cases.
2. Inducing rules from cases by computational means -
the ID3 algorithm.
D. Working with case II.
1. The use of frames and semantic nets.
2. Dimensions and the HYPO project.
3. Prototypes and Deformations.
E. Predictive and Normative Expert Systems - What can
Legal expert systems safely do?
F. Conceptual Retrieval - Going beyond the constraints imposed
by full text retrieval using boolean logic.
G. Intelligent Document Assembly - How computer science can
safely achieve enormous efficiencies in the here and now.
DATES: 2-6 p.m. 8,9,10,11,12,15,16,17,18 and 19 July 1991.