KDD Nuggets Index
To KD Mine: main site for Data Mining and Knowledge Discovery.
To subscribe to KDD Nuggets, email to kdd-request
Past Issues: 1996 Nuggets,
Data Mining and Knowledge Discovery Nuggets 96:12, e-mailed 96-04-09
IBM Announces Data Mining Solution for Improved Decision,
L. Bookman, APT introduces Parallel Data Warehousing
and Data Mining Environment
R. Sun, Report on Hybrid Connectionist-Symbolic Models,
R. Kohavi, 3D Decision-Tree Visualizer for MLC++,
B. Wagstaff, Data Mining intern position at Red Brick Systems,
J. Fuernkranz, CFP: MLnet WS on ILP for KDD,
M. Klusch, CIA-97 Workshop on Cooperative Information Agents
postponed from Oct 96 to Feb 97,
Data Mining and Knowledge Discovery community,
focusing on the latest research and applications.
Contributions are most welcome and should be emailed,
with a DESCRIPTIVE subject line (and a URL, when available) to (email@example.com).
E-mail add/delete requests to (firstname.lastname@example.org).
Nuggets frequency is approximately weekly.
Back issues of Nuggets, a catalog of S*i*ftware (data mining tools),
and a wealth of other information on Data Mining and Knowledge Discovery
is available at Knowledge Discovery Mine site, URL http://info.gte.com/~kdd.
-- Gregory Piatetsky-Shapiro (moderator)
********************* Official disclaimer ***********************************
* All opinions expressed herein are those of the writers (or the moderator) *
* and not necessarily of their respective employers (or GTE Laboratories) *
~~~~~~~~~~~~ Quotable Quote ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
In the middle of a difficulty lies an opportunity
A. Einstein (?)
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Date: Wed, 3 Apr 1996 09:16:25 -0800 (PST)
From: Sara Hedberg (email@example.com)
To: Gregory Piatetsky-Shapiro (firstname.lastname@example.org)
Subject: FW: Announces Data Mining Solution for Improved Decision Making (fwd)
IBM Announces Data Mining Solution For Improved Decision Making; New
Ammo For Knowledge Discovery And Validation Of Business Intelligence
SOMERS, N.Y.--(BUSINESS WIRE)--April 2, 1996--
IBM today announced new decision support offerings that allow
customers to 'mine' their data assets in search of high value business
intelligence, such as hidden relationships, new trends and previously
undetected patterns, that can give them a competitive edge. The new
products and services include:
- The IBM Intelligent Miner, a knowledge discovery toolkit for
analyzing, extracting and validating data traditionally held in data
- The IBM Intelligent Decision Server, a LAN-based information
analysis server to deploying decision support applications throughout
- A number of customizable, cross-industry applications; and
- Consulting and services support to help customers in a wide range of
industries take advantage of knowledge discovery and validation
'Business intelligence is not just about building a data warehouse;
it's about detecting something that you didn't know before,' said Tim
Negris, vice president, Sales and Marketing, IBM Software Solutions
Division. 'Now all types of companies -- both large and small -- can
leverage the tremendous business benefits that were previously only
available to large corporations who could afford customized data
mining and business intelligence solutions.'
'Our studies show a real need for this type of technology in
retailing, diversified financial services, telecommunications, and
other markets,' said Aaron Zornes, executive vice president of META
Group's Application Development Strategies. 'During 1996, we believe
that IBM's data mining toolkit and related service offerings will set
the pace for large-scale business technology initiatives in data
IBM Intelligent Miner
The IBM Intelligent Miner toolkit consists of powerful
algorithms and processing techniques that enable application
developers to analyze data stored in flat files or databases, such
as IBM's DATABASE 2 Parallel Edition (DB2 PE)(a). The Intelligent
Miner algorithms enable analyses ranging from deviation detection,
classification and predicative modelling, to association discovery,
sequential pattern discovery and database segmentation.
Through deviation detection, for example, a financial
services company could quickly and easily detect fraudulent usages of credit
cards by examining deviations in the credit-card usage patterns of
its customers. Using predictive modelling, a retailer could
forecast changes in customer buying patterns and keep abreast of
comparisons of purchases over the Internet or through mail-order
with those through in-store buying. Through association discovery,
a supermarket chain could determine which products are most
frequently sold in conjunction with other products, and stock these
store items on shelves accordingly to maximize sales opportunities.
An insurance company could use customer segmentation data to create
target-marketing campaigns, or to cross-sell services among existing
customers. Sequential Pattern analyses could help medical
researchers identify common patterns of symptoms that lead to
The Intelligent Miner also includes an extensive pre-processing
library of tools to prepare that data for mining and verification.
To improve the data analyst's productivity, these tools can be
invoked dynamically, without coding, during the iterative process of
preparing, mining and verification. They include data selection,
transformation and cleansing. Additionally, a set of interactive
visualization tools can be used to bring out unusual features that
might otherwise be 'drowned out.'
'Businesses are facing competitors that are not just across
town or across the country -- but around the world as well
non-traditional competition over the World Wide Web,' said Ben
Barnes, general manager, IBM Worldwide Decision Support Solutions.
'Data mining is quickly being recognized as an essential business
intelligence tool. IBM data mining tools and solutions can provide
the knowledge necessary to improve a company's market presence and
differentiate their products and services in today's global
Intelligent Decision Server
The Intelligent Decision Server (IDS) provides an affordable,
easy-to-use solution to both Internet and Intranet users. It allows
them to develop and distribute decision-support applications based
on advanced analytics. With IDS, for example, a sales manager could
request a customized market share report using a familiar browser
front-end system such as Lotus Notes(b) or a World Wide Web. The
request is sent to the application server, processed and returned as
a customized report. Meanwhile, analysts may also be drawing on the
same server -- this time using high-end analytical clients. Any
change in calculation logic or any new data discovery algorithm is
automatically updated for all users with no change in the client
Application development within IDS is done in a graphical,
icon-based environment, using transformers (executable objects) to
perform distinct functions within the Intelligent Miner itself, this
speedy development environment also fosters the reuse of
object-oriented decision support applications that can be executed
from any common network client software, including the Web, Lotus
Notes, OS/2(a) and Windows(b). IDS's open Application Program
Interface (API) enables support vendors, such as Cognos, Business
Objects, Andyne, Brio and others, to work on product integration.
IBM has developed three customizable, cross-industry
applications. These applications include:
- Customer Segmentation -- segments and scores customers data from
marketing databases, including private and public data sources,
in an effort to better understand customer behavior. Results are
used for target marketing, cross-selling, customers retention
campaigns, propensity to purchase and consumer vulnerability
- Item Set Analysis -- also known as market basket analysis, this
application aims to understand customer buying behavior, and to
predict their future behavior by identifying affinities among
their choice of products and services.
- Fraud Detection -- this application identifies deviations from
established usage norms in order to flag suspicious transactions
which may be indicative of fraudulent activity.
Business Intelligence Consulting and Services
Customers can take advantage of extensive IBM Consulting &
Services expertise in designing, integrating and testing data mining
solutions for a wide range of industries, including retail, banking,
financial services, healthcare, travel, telecommunications and
Beta testing of the Intelligent Miner and of the applications on
IBM RS/6000 servers with AIX and client versions on AIX, Windows
3.1(b) and Windows 95 platforms will begin in April 1996. General
availability is scheduled for the third quarter 1996. Versions for
the AS/400 and S/390 platforms are targeted for later in 1996.
Pricing will be announced at a later date.
The data mining technology will be available to third party
VARs, System Integrators and ISVs for outsourcing research projects,
as well as tailored business intelligence application development.
IDS leverages the core technology of IBM's currently
decision support product, Data Interpretation System (DIS), and will
begin beta testing in June 1996. The OS/2-based server supports a
variety of clients including OS/2, Windows 3.1, and Windows 95.
Pricing is not yet determined.
IBM's Software Solutions Division provides data management,
application development and workgroup solutions for mission-critical
applications on personal computers, workstations, LANs and host
systems. For Internet users, IBM offers complete information about
the company, its products, services and technology on the World Wide
Web. The IBM home page is accessible via http://www.ibm.com.
fastest, easiest way to get information about IBM software is to go
to the IBM Software home page at http://www.software.ibm.com.
IBM's Worldwide Decision Support Solutions unit provides
marketing applications, consulting and services focused on decision
support and data mining computing solutions that apply advanced
search analysis and data management techniques databases. These
solutions are revolutionizing the way businesses make use of
information to make decision more intelligently and effectively.
(a) Indicates trademark or registered trademark of International
Business Machines Corp.
(b) Indicates trademark or registered trademark of respective
CONTACT: IBM Media Relations
Susan Scott-Ker, 914/766-1463
Brodeur & Partners
Beth Kitchener, 617/622-2800
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Date: Tue, 2 Apr 1996 16:39:34 +0500
Subject: Announcement for data mining summit
Applied Parallel Technologies
Here is the annoucement:
Robert Utzschneider, APT
(617) 494-1177 ext. 116
Bill O'Leary, IBM
APT INTRODUCES PARALLEL DATA WAREHOUSING
AND DATA MINING APPLICATION DEVELOPMENT ENVIRONMENT
FOR IBM RS/6000 SP SYSTEM
BOSTON, Massachusetts, March 26, 1996 -- Applied Parallel Technologies,
(APT) today announced ORCHESTRATE*, an advanced data mining application
development environment for the IBM RS/6000** Scalable POWERparallel**
(SP**) server using either IBM DB2** Parallel Edition** (PE**) or Oracle
Parallel Server relational database management systems.
APT has designed the ORCHESTRATE Development Environment to support
construction of large-scale data warehousing and data mining software
systems which fully exploit the capabilities of parallel computing and
parallel RDBMS systems. By hiding the complexities of both parallel
programming and the handling of large data, ORCHESTRATE enables commercial
systems integrators and application developers to build these systems
faster and at a far lower cost.
'ORCHESTRATE represents an important addition to the RS/6000 SP system
software environment' stated Ben C. Barnes, general manager of IBM's newly
worldwide Decision Support Solutions organization. 'By making it simpler
for application developers and systems integrators, including IBM, to build
data mining software products and systems for the RS/6000 SP, APT and IBM
can accelerate customer exploitation of large databases.'
Data mining is the process of discovering hidden patterns and
relations in large databases using a variety of advanced analytical
techniques. APT intends to take advantage of the rich set of these
techniques offered by IBM. In many applications, data mining offers
distinct advantages over more traditional decision support techniques
such as querying (e.g. SQL querying) and statistical analysis.
Examples of problems which can be addressed using data mining software
include finding fraud in credit card transactions, predicting which
subscribers are likely to drop a magazine subscription, and
identifying customer buying patterns in retailing chains.
Customer demand for large-scale data mining is growing
quickly. Businesses are generating increasing volumes of data about
their customers. Rapid declines in disk storage costs now make it
possible to store massive amounts of data. Parallel computing and
RDBMS systems provide the enabling technology required for building
large-scale data warehouses and applying complex data mining
techniques against the data. However, the complexities of both
parallel programming and operating on large data has limited the
development of data warehousing and data mining software systems which
fully exploit the information contained in very large databases.
ORCHESTRATE facilitates migrating data from source repositories to the
RS/6000 SP server and performing complex data conversions and
transformations prior to loading the data warehouse. ORCHESTRATE also
supports parallel data connections between advanced data mining
applications and large-scale data warehouses based on DB2 Parallel Edition
and Oracle Parallel Server. ORCHESTRATE includes an expanding analytical
toolkit, incorporating neural networks and proprietary techniques, for
parallel data mining. ORCHESTRATE also supports more traditional decision
ORCHESTRATE provides the capability to run sequential third-party software
packages in parallel transparently across the processors of the SP server.
This allows software vendors and systems integrators to leverage the
functionality of leading data warehousing and data mining software tools
against large data without the complexities of running multiple instances
across processing nodes of a parallel computing system. To provide the
performance advantages of sorting large data sets in parallel, APT has
integrated SyncSort, the high-performance sort product from Syncsort,
Inc., Woodcliff Lake, NJ, into ORCHESTRATE.
Coinciding with the product announcement, APT and IBM also
announced a joint initiative to support third-party software
developers and systems integrators in building data mining software
tools and systems for SP systems and ORCHESTRATE. The program intends
to support a variety of software groups: vendors of sequential data
warehousing and data mining software tools; software developers
interested in building advanced data mining software tools using
ORCHESTRATE; and systems integrators building large-scale data
warehousing and data mining software systems. Details about this
initiative are available from APT. APT plans to release the initial
commercial version of ORCHESTRATE by the end of the second quarter of
The RS/6000 platform, IBM's high-performance line of technical and
commercial workstations and servers offer customers one of the most
extensive families of UNIX-based solutions available in the marketplace.
From PowerPC-based workstations and servers for small and large businesses
to symmetric multiprocessor and parallel computing systems for the most
demanding of client/server or commercial on-line transaction processing
applications, the RS/6000 offers powerful performance and price/performance
IBM's Software Solutions Division provides data management, application
development and workgroup solutions for mission-critical applications on
computers, workstations, LANs and host systems.
# # #
* Indicates trademark of Applied Parallel Technologies, Inc.
** Indicates trademark or registered trademark of International Business
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Subject: a report on Hybrid Connectionist-Symbolic Models
Date: Mon, 25 Mar 1996 14:11:37 -0600
Hybrid Connectionist-Symbolic Models:
a report from the IJCAI'95 workshop on connectionist-symbolic integration
Department of Computer Science
The University of Alabama
Tuscaloosa, AL 35487
To appear in: AI Magazine, 1996.
ftp or Mosaic access:
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Date: Tue, 9 Apr 1996 09:11:09 -0700
Subject: 3D Decision-Tree Visualizer for MLC++
Under a trial program, Silicon Graphics is now giving research
institutes free licenses for the MineSet(TM) Tree Visualizer program
for use with MLC++, the machine learning library in C++.
Tree visualizer provides a 3D fly-through of trees generated by the
MLC++/ID3 algorithm. The program will only work on Silicon Graphics
Visit our web page (you must use netscape or a browser that supports forms):
for more information. Click on 'tree visualizer' for details.
Ronny Kohavi (email@example.com,
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Date: Mon, 01 Apr 1996 16:12:20 -0800
From: Bill Wagstaff (bmw@RedBrick.COM)
Subject: Data Mining intern position available at Red Brick Systems
Red Brick Systems Incorporated, developer of a high performance relational
database server for dataware housing is looking for an intern for the 1996
summer to help develop prototype data warehousing/data mining applications.
The ideal candidate would have familiarity with both relational database
systems and data mining. Also important would be a long term interest in
either or both fields.
Red Brick Systems Inc. is located in Los Gatos, CA (near San Jose). General
information about the company can be obtained from our website at
Interested candidates should send e-mail to 'firstname.lastname@example.org'.
should include 'datamining intern' in the subject line.
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Date: Mon, 8 Apr 1996 10:59:01 -0700
From: Patricia Riddle (email@example.com)
Subject: Job Opening
* Outstanding Applied Researchers needed *
The Boeing Company, the world's largest aerospace company, is
actively working research projects involving NASA, FAA, Air Traffic
Control, and Global Positioning as well as airplane and manufacturing
research. The Research and Technology organization located
in Bellevue, Washington, near Seattle, has positions open.
We are the primary computing research organization for Boeing.
We have contributed heavily to both short term technology advances and
to long range planning and development.
- Machine Learning
BACKGROUND REQUIRED: Data Mining, Knowledge Discovery
Statistics, Artificial Intelligence or related
RESEARCH AREAS: We are developing techniques for mining a very diverse
set of data: Safety Data -- safety incident data, flight data
recorders; Reliability Data -- maintenance actions, airplane
maintenance warnings; Manufacturing Data - rejected parts, quality
assurance data. These are not areas where most large R&D datamining
efforts are currently focused. There are many new innovative research
directions which are not being addressed elsewhere. At the same time,
we can achieve major practical impacts in the short-term both at
Boeing and in the airline industry as a whole: airplane or factory
redesign, new pilot regulations or training, which may result in a
safer more cost effective air travel industry.
- Knowledge Representation
BACKGROUND REQUIRED: A strong background in Artificial
Intelligence plus some specialization in Knowledge
Representation and Reasoning, Ontology Development, Knowledge
Based Engineering, Knowledge Sharing and Reuse or related
RESEARCH AREAS: Knowledge Based (KB) design methods are gaining
acceptance as a way to implement standard design methods used in
all parts of the design lifecycle. We are developing methods for
neutral representation of design rules, methods to capture and
refine specifications, and are looking for ways to capture and
manage design assumptions. The design of aircraft is one of the
most challenging tests of knowledge based methods: an airplane
consists of millions of parts, design is subject to rigorous
certification, and more designed tooling is used than for almost
any other application.
A PhD in Computer Science or equivalent experience is highly
desirable for both positions. We strongly encourage diversity
in backgrounds including academic and industrial experiences.
APPLICATION: If you meet the requirements and you are interested, please
send your resume via electronic email in plain ASCII format to
The Boeing Company is an equal opportunity employer.
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Date: Wed, 03 Apr 1996 11:05:19 +0200
From: Johannes Fuernkranz (firstname.lastname@example.org)
MLnet Sponsored Familiarization Workshop
Data Mining with Inductive Logic Programming
(ILP for KDD)
In conjunction with the 13th International Conference on Machine Learning
July 2 1996 (full day), Bari, Italy
Many standard methods for Knowledge Discovery in Databases (KDD) are
constrained to processing a single relational table, whereas many real-world
databases are structured into several tables containing interrelated
information. As Inductive Logic Programming (ILP) algorithms explicitly aim
at exploiting structured information, KDD should be a fruitful research and
application area for ILP. Yet, ILP algorithms seem to be rarely used in KDD.
The goal of this workshop is to evaluate the potential contribution of ILP
for KDD. We hope to bring together researchers from both areas in order to
discuss questions like
* Are there any ILP successes on real KDD problems so far?
* Is there a need for ILP in KDD?
* Do ILP applications scale up to real databases?
* Why are so few KDD applications using ILP?
* Which ILP algorithms are of use for KDD purposes?
* Which type of algorithms should be further investigated?
* Which type of databases require ILP methods?
* Is extensive pre-processing of the data necessary for ILP methods to be
Each contribution that discusses one or more of these issues is welcome as
are research proposals and work in progress in this area. Note, however,
that applications of the UCI repository type are not of interest for this
Submissions (full papers or extended abstracts) should be sent (preferably
by e-mail) to the contact address below by MAY 15, 1996. PostScript or HTML
submissions are strongly encouraged. Submitted papers will be made available
via the WWW before the workshop in order to allow participants to prepare
for discussions. Those who wish to attend the workshop without presenting a
paper should send a short note that they intend to register and should
include a one-paragraph description of their research interests.
MLnet WS ILP for KDD
Johannes F|rnkranz and Bernhard Pfahringer
Austrian Research Institute for Artificial Intelligence
A-1010 Vienna, Austria
Phone: (+43 1) 533-6112-0
Fax: (+43 1) 532-0652
Papers due: May 15 1996
Notification of acceptance: June 1 1996
Submission of CRCs for working notes: June 15 1996
Workshop July 2 1996
L. De Raedt
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Date: Mon, 1 Apr 1996 11:13:25 +0200
From: Matthias Klusch (email@example.com)
Subject: CIA-97 Intern. Workshop on Cooperative Information Agents
Mailer: Elm [revision: 70.85]
ATTENTION: Postponement of CIA-Workshop from October 1996 to February 1997.
Final Time Schedule in the following announcement.
ANNOUNCEMENT and CALL FOR PAPERS
1. International Workshop CIA-97
COOPERATIVE INFORMATION AGENTS - DAI meets Database Systems
26th (Wed) - 28th (Fri) of February 1997
University of Kiel, Computer Science Department,
The workshop will be held in cooperation with the research group FG 1.1.6 on
Distributed Artificial Intelligence (DAI) and FG 2.5.1 on Database Systems
of the German Society for Computer Science GI.
This workshop will focus on issues concerning approaches for an integrated
use of methodologies from both research areas, DAI and Database Systems,
especially for the development of cooperative information agents.
The idea of a partial synthesis of both, the DAI as well as the Database
research area seems to be very promising. One attempt to capture possible
benefits from such a synthesis has lead to the introduction of the paradigm
of Cooperative Information Systems (CIS) in 1992.
In our context a CIS is constituted by a set of intelligent agents where
each of them is uniquely attached to one database system. Such information
agents behave like active, intelligent database front-ends trying to satisfy
their own application-specific task goals alone or in cooperation with others.
In particular the necessity to respect the database autonomy requirements
hinders such cooperation e.g. for information gathering.
The design of information agents inherently requires knowledge from several
different research areas like DAI, Database and Expert Systems, and AI.
Unfortunately, practical and theoretical work which is relevant for the
development of information agents tends to be scattered across several
different forums of respective computing subareas: There is an obvious need
for a survey of these works, their advantages and limitations.
Thus, the workshop aims for being a small but intensive forum for a presentation
and exchange of ideas, work in progress, reviews as well as an engaged discussion
between all attendees.
TOPICS of interest include, but are not limited to:
o architecture of information agents
o autonomy requirements and their impacts for the development of
information agents and systems
o decentralized construction and management of common ontologies
for cooperative information agents
o knowledge discovery and data mining for information evolution
in large database networks
o semantic querying in multidatabase systems
o use of object-oriented modelling within the design of information agents
o methods for coalition formation among autonomous agents
o approaches towards a theory of organization in multi-agent systems
o adaptation and self-organization of information agents
o planning in cooperative search for information
o user interface issues for information agents
o evaluation and development environments for information agents
o agent communication languages (like KQML/KIF)
o approaches towards mobile information agents (e.g. Telescript, Java)
Authors are invited to submit papers describing both theoretical and practical
work dealing with the use of methods from distributed artificial intelligence
for cooperation between a set of heterogeneous, autonomous databases.
Papers describing ongoing research are in particular welcome.
Topics of interest include, but are not limited to the ones listed above.
The paper must be in A4 size (basic font 12 point).
Each submission must have a separate title page and a body. The title page must
include a title, a 300-400 word abstract, a list of keywords, the names and
addresses of all authors, their email addresses, and their telephone and fax numbers.
The body must also include the title and abstract, but the author information
must be excluded. The length of submitted papers (excluding the title page) must
be no more than 12 single-spaced, single-column pages including all figures, tables,
and bibliography. All papers must be written in English.
and neither accepted nor under review by other conferences or journals.
Papers not conforming to the above requirements may be rejected without review.
Papers can be submitted both by mail and electronic mail. Electronic submission must
be in postscript format.
Send three hard-copies or the postscript file of your contribution to
Institut f'ur Informatik und Praktische Mathematik
Christian-Albrechts-Universit'at zu Kiel,
Olshausenstr. 40, 24118 Kiel
In order to inform us about your submission send an email to:
All accepted papers will be published in the proceedings of the workshop.
Paper Submission Deadline : 3.11.1996
Notification to the Authors : 15.12.1996
Camera Ready Due : 20.1.1997
The number of participants will be restricted to atmost 80 peoples.
Preference is given to people with accepted contributions. Early registration is
recommended. For registration please send your name, full address (incl. phone/fax),
affiliation, and a short description about your current research interests to the
Organization Committee (see below). Let us also know if you intend to submit a paper.
DATE AND LOCATION:
The workshop starts on Wednesday, 26.2.1997, at 8 am and ends on Friday, 28.2.1997,
afternoon. It takes place at the University of Kiel.
(Address: CAU Kiel, Institut f'ur Informatik, Olshausenstr. 40, 24118 Kiel, Germany)
Nearest international airport is located in Hamburg. The city of Kiel is easily
reachable by train from Hamburg in about one hour.
A direct connection from the Hamburg airport to Kiel and back is provided by an
airport express bus (named 'Kielius').
Wolfgang Benn (University of Chemnitz, Germany)
Sonia Bergamaschi (Universita' di Modena, Italy)
Hans-Dieter Burkhard (Humboldt University Berlin, Germany)
Misbah Deen (University of Keele, UK)
Yves Demazeau (Leibniz/Imag/CNRS, France)
Frank Dignum (University of Eindhoven, The Netherlands)
Edmund Durfee (University of Michigan, USA)
Tim Finin (University of Maryland, USA)
Klaus Fischer (DFKI Saarbruecken, Germany)
Joachim Hammer (Stanford University, USA)
Peter Kandzia (University of Kiel, Germany)
Larry Kerschberg (George Mason University, USA)
Stefan Kirn (University of M'unster, Germany)
Matthias Klusch (University of Kiel, Germany)
Sarit Kraus (Bar Ilan University, Israel)
Klaus Meyer-Wegener (University of Dresden, Germany)
Aris Ouksel (University of Illinois, USA)
Mike P. Papazoglou (QUT Brisbane, Australia)
Jeffrey Rosenschein (Hebrew University, Israel)
Tuomas Sandholm (University of Massachusetts at Amherst, USA)
Onn Shehory (Bar Ilan University, Israel)
Antonio Si (Polytechnic University, Hong Kong)
Gottfried Vossen (University of Muenster, Germany)
Gerd Wagner (University of Leipzig, Germany)
Mike Wooldridge (Manchester Mteropolitan University, UK)
General Chairs and Organization Committee:
Matthias Klusch (University of Kiel, Germany)
Peter Kandzia (University of Kiel, Germany)
UP-TO-DATE-INFORMATION about the workshop will be given on the WorldWideWeb WWW:
For further information about the workshop please contact:\
Institut f'ur Informatik
Christian-Albrechts-Universit'at zu Kiel,
Olshausenstr. 40, 24118 Kiel
PHONE : +49-431-880-4474
Fax : +49-431-880-4054
EMail : firstname.lastname@example.org
WWW : http://www.informatik.uni-kiel.de/~mkl/
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