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, 1995 Nuggets, 1994 Nuggets, 1993 Nuggets

Data Mining and Knowledge Discovery Nuggets 96:34, e-mailed 96-11-07

News:

* C. Shearer, Clementine Strikes Silver
Publications:

* GPS, IEEE Expert Oct'96 Spec. Issue on Data Mining,

Searching for Mother Lode: Tales of the first Miners, by S. Hedberg,

http://www.computer.org/pubs/expert/1996/insight/x5004/x5004.htm

* R. Kohavi, MLJ spec. issue on Applications of Machine Learning

and the Knowledge Discovery Process,

* T. Catarci, SIGMOD record special issue on Information Visualization

http://www.cs.tufts.edu/~isabel/print.html
Siftware:

* S. Reiss, SGI MineSet 1.1,

http://www.sgi.com/Products/software/MineSet

* F. Lenherr, Neuroscience Web Search

http://www.acsiom.org/nsr/neuro.html

* M. Kiselev, PolyAnalyst free evaluation version available,

http://mosca.sai.msu.su/~mp/megapute.html
Positions:

* W. Cohen, Job offer for research programmer at AT&T Labs

* J. Gavan, Telecom Knowledge Discovery positions in Western US
Meetings:

* M. Berthold, IDA-97: Intelligent Data Analysis,

London, 4-6 August 1997, http://web.dcs.bbk.ac.uk/ida97.html

* M. Klusch, CIA-97: Cooperative Information Agents,

extended submission deadline: 17 November 1996

http://www.informatik.uni-kiel.de/~mkl/cia97.html

--
Discovery in Databases (KDD) community, focusing on the latest research and
applications.

Submissions are most welcome and should be emailed,
with a DESCRIPTIVE subject line (and a URL, when available) to kdd@gte.com
To subscribe, email subscribe kdd to kdd-request@gte.com.

Nuggets frequency is approximately 3 times a month.
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 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 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Latest web jargon
> CGI Joe
>A hard-core CGI script programmer with all the social skills and charisma
>of a plastic action figure.

> Egosurfing
>Scanning the net, databases, print media, or research papers looking for
>the mention of your name.

> Nyetscape
>Nickname for AOL's less-than-full-featured Web browser.

> Cobweb Site
>A World Wide Web Site that hasn't been updated for a long time. A dead
>web page.
(thanks to John Vittal)

Previous 1 Next Top

>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
From: Colin Shearer (colin@isl.co.uk)
Date: Mon, 4 Nov 96 18:29:12 GMT
Subject: Clementine Strikes Silver

[Basingstoke, UK - Released 10th October '96]

CLEMENTINE WINS 'COMPUTING' AWARD FOR EXCELLENCE

Clementine, the data mining tool from Integral Solutions Ltd. (ISL) received
the Silver Award for Technology Transfer of the Year in 'Computing' Magazine's
Awards for Excellence 1996. The panel of judges was impressed by Clementine,
a data mining tool which converts data into business decisions. It helps
companies realise the value in their data and derive business benefit from
data warehousing.

The judges were looking for products which turned research results into
commercially viable products. Clementine takes several machine learning
techniques from artificial intelligence - neural networks and rule induction -
and packages them into a tool which can be used by business people who need
have no knowledge of the technology. The key to this is visual programming.
ISL's data mining director Colin Shearer says 'everything in Clementine is
done simply by connecting up icons which perform different functions on data'.

Clementine applications include fraud detection, retail basket analysis,
customer loyalty, prediction of toxicity, targeting direct mail, improving
mail shot response, predicting television audience share.

ISL Marketing Director Linda Montgomery remarks, 'We are especially delighted
that a UK developed software product has been recognised as world class.
Clementine is ranked alongside Sun's JAVA, Microsoft's Windows 95 SQL Server,
Oracle V7 and SAP'.

The Gold Award for Technology Transfer went to Cambridge Display Technology
for their organic light emitting diodes.

Previous 2 Next Top

>~~~Publications:~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Wed, 30 Oct 1996 14:09:42 -0500
From: Gregory Piatetsky-Shapiro (gps0@gte.com)
Subject: IEEE Expert Oct'96 Spec Issue on Data Mining

This issue has a number of articles available online.
In particular, Sara Hedberg has written a nice survey of first
data mining applications,
entitled 'Searching for the mother lode: tales of the first data miners',
see http://www.computer.org/pubs/expert/1996/insight/x5004/x5004.htm

highlighting
* Washington State's Department of Social and Health Services
analysis of foster children data
(see http://info.gte.com/gtel/sponsored/kdd/nuggets/95/n7.txt for
original request by T. Clark which helped start this application)

* IBM's Advanced Scout for data mining the NBA,

* US Treasury system for stopping money laundering

* and the experience of users of well-known SKICAT system
for mining the sky data,

Previous 3 Next Top

>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Wed, 30 Oct 1996 23:09:06 -0800
From: Ronny Kohavi (ronnyk@starry.engr.sgi.com)
Subject: CFP: Special Machine Learning issue on applications of ML

Call For Papers

Machine Learning

Special Issue on
Applications of Machine Learning
and the Knowledge Discovery Process

Guest editors: Ronny Kohavi and Foster Provost

With the explosion in size of business and scientific databases
(VLDBs), the opportunities and pressure to mine the data and make
novel discoveries have increased dramatically. For many problems,
basic statistical summaries are not sufficient and there is a clear
and recognized need for solutions involving a machine learning
component. For example, modern businesses constantly seek to gain
competitive advantage by tailoring actions to different customer
segments and avoiding the trap of targeting the 'average customer.'

This special issue of the journal Machine Learning will be dedicated
to papers describing work in which machine learning technologies have
been applied to solve significant real-world problems. In particular,
it will focus on the application of Machine Learning technology, the
simplifying assumptions that *cannot* be made in a real-world
application, and the processes that are involved in going from the raw
data to the final knowledge that decision makers seek.

--------------- Scope ---------------

High quality, original papers that address applications of machine
learning technologies to significant, real-world problems are
solicited. Authors are required to describe the papers' scientific
contributions clearly. We actively solicit papers that address
contributions not normally published in the machine learning
literature, including (but not limited to):

- - Applications of machine learning to real-world significant problems:
successes, failures, limitations, and lessons learned.

- - The knowledge discovery process: it is estimated that only
20% of the overall process is spent on running machine
learning algorithms. What other things were done? What
lessons were learned? Which parts of the overall process could be
improved and perhaps automated?

- - A novel solution to a non-trivial _class_ of applications. Such a
paper would include: a description of the class of applications,
detailing why they are different from previously solved
applications; an 'existence proof' on one member of the class;
and a solid argument as to the generality of the solution for
the class of applications.

- - The identification of a number of assumptions normally made within
machine learning research that cannot be made for this application,
and a thorough description of how they were addressed (simultaneously)
to engineer a working solution.

- - A principled study of the relaxation of important assumptions
necessary for the solution to the problem.

- - Novel uses of existing algorithms and modifications
to broaden their scope and overcome limitations and assumptions
that hinder their use in real applications.

Authors should address the following issues, when relevant:

- - The real-world problem and its formulation as a machine learning
task. How significant was the problem?
- - Who are the users of the learned knowledge? Who 'paid' for the work?
How did their requirements constrain the knowledge discovery effort?
How was success to be determined and was the expected
payoff estimated in advance?
- - The data selection process. Was a sampling process used or were all
the data used? What were reasons for limiting the amount of data
(resource constraints, simplicity of result, learning curve flattened
early)?
- - Data transformations. How were the raw data transformed into
formats suitable for existing algorithms? What processes were required,
why were certain transformations done, and were others tried?
Examples: data cleaning, missing values, data transformations,
aggregrations, group signatures/profiles, denormalization,
and feature construction.
- - Why were specific algorithms chosen, and which others were tried?
- - What role did background knowledge play and how has it affected
the process?
- - What were the post-processing operations? How were the results
explained to users (visualizations, dimensionality reduction,
explanations, what-if scenarios, sensitivity analyses)?
- - How did the results affect the target users? Was the mining done
repeatedly, or was this a one-shot task? Was the learned knowledge
or the learning system fielded and integrated in other processes?

The contribution of each paper should be sufficient to lay the
groundwork for future work on the topic. Future applied work can
build on the initial solutions described; future academic work can
explore the exposed assumptions in depth and provide more principled
solutions. We encourage authors to suggest 'challege problems' for
future academic work. For example, can a useful 'transformation
language' be developed in a manner similar to the role relational
calculus has served for relational databases?

We hope that by providing the machine learning community with such a
forum, we can stimulate the applied/academic cycle that is important
for the healthy growth of a scientific field.

--------------- Submission Requirements ---------------

The Machine Learning journal is published by Kluwer Academic
Publishers. Electronic submissions are ENCOURAGED; postscript copies
may be e-mailed to mljapps@postofc.corp.sgi.com. Latex style file and
related files available via anonymous ftp from ftp.std.com in
Kluwer/styles/journals.

For non-electronic submissions, send six (6) copies of the papers
as indicated:

Five (5) copies to: One (1) copy to:

Mrs. Karen Cullen Ronny Kohavi
MACHINE LEARNING Silicon Graphics, Inc, M/S 8U-876
Kluwer Academic Publishers 2011 N. Shoreline Boulevard
101 Philip Drive Mountain View, CA 94043
Assinippi Park
Norwell, MA 02061 E-mail: ronnyk@sgi.com
Tel: 415-933-3126

Manuscripts should be no longer than 8000-12000 words with full-page
figures counting for 400 words. Shorter submissions, including
technical notes are also solicited. The title page should include names,
affiliations, and complete address including daytime telephone number
and an electronic e-mail address of the contact author. Include a
brief, one-paragraph abstract of 100-200 words and a list of keywords.

Submissions must not have appeared in, nor be under consideration by,
other journals.

--------------- Important Dates --------------------

Submission deadline: 4 Mar 1997
Acceptance notification: 3 June 1997

** Review criteria and further details will be sent in the future.

Specific questions and clarifications should be sent to
mljapps@postofc.corp.sgi.com

Previous 4 Next Top

>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Fri, 25 Oct 1996 18:09:12 +0100
From: 'Tiziana Catarci' (catarci@infokit.dis.uniroma1.it)
To: VISUAL-L@VTVM1.CC.VT.EDU, diagrams@csli.stanford.edu,
announcements.chi@xerox.com, kdd%eureka@gte.com, infodesign@uva.nl
Subject: Special Issue on Information Visualization

The December 1996 issue of Sigmod Record will be on Information Visualization.

If you are interested in this topic, please give a look at:

http://www.cs.tufts.edu/~isabel/print.html.

Best Regards,

Tiziana Catarci
Dipartimento di Informatica e Sistemistica
Universita' degli Studi di Roma 'La Sapienza'
Via Salaria 113, 00198 Roma
ITALY

Tel. +39-6-49918331
Fax. +39-6-49918331 or +39-6-85300849
E-mail catarci@infokit.dis.uniroma1.it or tic@cs.brown.edu
URL http://www.dis.uniroma1.it/AVI96/tchome.html

Previous 5 Next Top

>~~~Siftware:~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
From: 'Steve Reiss' (sreiss@powerplay.engr.sgi.com)
Date: Thu, 24 Oct 1996 14:13:34 -0700
To: kdd@gte.com
Subject: SGI Mineset 1.1

Included here is some new information for MineSet. We have just released
MineSet 1.1 which is a significant upgrade from our first release.

Also, please note that the URL for MineSet has changed. If you could update
this in all of your links, it would be greatly appreciated.

By the way, thanks for providing this site as a resource to the rest of us.

--steve

Steven Reiss Silicon Graphics, Inc.
sreiss@sgi.com 2011 N. Shoreline Blvd
voice 415-933-2043 P.O. Box 7311
fax 415-390-6320 Mail Stop 855
Mountain View, CA 94039

Siftware: MineSet

*URL: http://www.sgi.com/Products/software/MineSet
*Description: MineSet(tm) version 1.1 is the second release of SGI's
product for exploratory
data analysis. MineSet integrates tools for data access and transformation,
data mining, and visual data mining. In particular:

Direct access to Oracle, Informix, Sybase, and flat file data.
Data transformation support for binning, aggregations, distribution, column
removal, and new column creation.
Association Rule generation.
Decision tree induction
Naive -Bayes induction
Application of classifiers directly to data.
3-dimensional, animated, interactive visualizations for geographical,
multi-dimensional,
and hierarchical data.
3-dimensional, animated, interactive visualizations for decision tree,
naive-Bayes,
and association rule representation and analysis.

Users have complete flexibility to decide what data to access, how to
transfrom
data, how to mine the data, and how to apply the results to visualizations.
MineSet encourages interactive and iterative exploration of data.

*Discovery tasks: Classification, Associations, Clustering,
Profiling, Segmentation, Summarization,
Deviation Detection, Visualization

*Comments: An important component of exploratory data analysis is visual
data mining.
The ability to represent and explore data visually allows you to quickly and
intuitively
discover relationships and trends which are impossible to determine using
traditional data access
and data mining techniques. Using 3D and animation, data comes to life.

Visualization also plays a key role in the data mining process. By providing
a higher level analysis of trends and relationships in data, visualization
helps determine
which subsets of large datasets are most likely to be interesting as input to
the data mining process.
Visualizations of the data mining results allow you to do further analysis,
discovering new meta-knowledge
and gaining increased understanding of the generated knowledge models.

MineSet provides support for development of visualizations, exploration
and navigation of visualizations, and display and analysis of data mining
results.

MineSet uses many algorithms from MLC++

*Platform(s): Silicon Graphics workstations. Client software can be run in X server mode on Windows NT
systems with X and OpenGL servers running.

*Contact: Steven Reiss, phone
415-933-2043, fax 415-390-6320,

*Status: Product

*Source of information:
Reiss

*Updated: 1996-10-24 by Steve Reiss,

Previous 6 Next Top

>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
From LISTSERV@hermes.csd.unb.ca: INDUCTIVE Digest - 27 Oct 1996 to 30 Oct 1996]

Date: Tue, 29 Oct 1996 22:29:14 -0400
From: Fred Lenherr (lenherr@mildura.cs.umass.edu)
Subject: Neuroscience Web Search

Hello,

I have created a new Web Search Engine devoted entirely to
neuroscience. Unlike the large search sites, everything here
is relevant by pre-selection.

The URL is:

http://www.acsiom.org/nsr/neuro.html

This is a full-text database and contains more than 55,000 web pages.

If you are interested, please take a look at it, and consider placing
a link to it on one of your own web pages.

Thanks very much,

Fred K. Lenherr

Previous 7 Next Top

>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Fri, 1 Nov 1996 18:08:48 +0300 (WSU)
From: 'Mikhail V.Kiselev' (megaputer@glas.apc.org)
Subject: Polyanalyst

Megaputer Intelligence, Ltd. is glad to announce that evaluation
version of its flagship DATA MINING product

###----> PolyAnalyst(tm) <----####

is now available from our ftp site.

To obtain PolyAnalyst evaluation package visit our WWW page

http://mosca.sai.msu.su/~mp/megapute.html

WE WISH YOU LUCK ANALYZING YOUR DATA WITH OUR EXCITING PRODUCT!!!

Comments are welcome to mailto:megapute@glas.apc.org

Mikhail Kiselev
Director R&D
Megaputer Intelligence

Previous 8 Next Top

>~~~Positions:~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Fri, 25 Oct 1996 14:12:10 -0400 (EDT)
From: William Cohen (wcohen@research.att.com)
Subject: job offer for research programmer at AT&T Labs

The department of Machine Learning and Information Retrieval Research
in AT&T Labs has an immediate opening for a research programmer. This
is a limited term (1-2) year position. Applicants should have a BS or
MS degree in computer science and experience programming in C/C++ in a
Unix environment. Experience with Java and Perl is desirable, as is a
strong mathematical background is desirable. Some experience
(e.g. projects or course work) in any of these areas is also
desirable: machine learning, information retrieval, pattern
recognition, neural networks, or artificial intelligence.

Applicants should have an interest in building robust implementations
of state-of-the-art methods for machine learning and text
categorization, and in interacting with both research and development
groups.

AT&T Labs-Research is located in Murray Hill, NJ but will soon be
moving to Florham Park, NJ. More information about AT&T Labs can be
found at http://www.research.att.com. AT&T is an Equal Opportunity
Employer.

If interested, send your resume to the following address. (A pointer
to a publically accessible web site is preferred.)

William W. Cohen
AT&T Labs-Research
600 Mountain Avenue
Room 2A-427
Murray Hill, NJ 07974

email: wcohen@research.att.com
WWW: http://www.research.att.com/~wcohen/

Previous 9 Next Top

>~~~Positions:~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Date: Wed, 30 Oct 96 14:41 EST
From: John Gavan (0003378944@mcimail.com)
Subject: AI Position Description

Dear KDD Moderator,

Would you please include this job posting in your next newsletter.
If any questions, I can be reached at &�-535-1916.

Thanks,

John Gavan

************************************************************
NEEDED: KNOWLEDGE DISCOVERY PROFESSIONALS THAT CAN
DELIVER TECHNOLOGY FOR COMMERCIAL APPLICATIONS
************************************************************

A large international Telecommunications company located in the western
US is seeking a few knowledge discovery/machine learning professionals
to form a core team to work on the design and development of a set
of pattern recognition engines to be used to monitor a real-time data
set of event records originating in a telephone network and identify
transactions that may be indicative of fraud.

This person or persons will be resonsible for analyzing a number of
labeled data sets and developing/formulaing AI algorithms to effectively
identify and capture fraud. Additional responsibility for directing
subcontractors in similar activities is also expected.

Strong artificial intelligence skills and experience in statistical
analysis, knowledge discovery and machine learning are required. Real
world experience in the use of clustering, neural networks, decision
trees and other techniques is needed. This team will be responsible
for analyzing various developed models and selecting 'best of breed'
algorithms for use in a state of the art fraud control system. It
is anticipated that this team will be employed to also work on data
mining and other knowledge discovery applications in the hope that
this will lead to other commercial applications of the technology.

The working environement is a large, campus complex in the Rocky Mountain
region. The position(s) offer highly competitive salary and benefits
and a the opportunity to work with a very strong team of application
developers. Opportunity for international travel in conjunction with
this project is also present. The location offers a wide range of
outdoor activities in a small metropolitan environment with a noted
quality of life.

For consideration, please send your resume or CV to:

JGAVAN@MCIMAIL.COM or fax to (719)535-4818

Previous 10 Next Top

>~~~Meetings:~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Subject: IDA-97 Call for Papers
Date: Mon, 28 Oct 1996 14:01:13 +0000
From: Michael Berthold (berthold@ira.uka.de)

CALL FOR PAPERS

The Second International Symposium on Intelligent Data Analysis (IDA-97)
Birkbeck College, University of London
4th-6th August 1997

In Cooperation with
AAAI, ACM SIGART, BCS SGES, IEEE SMC, and SSAISB

[ http://web.dcs.bbk.ac.uk/ida97.html ]

Objective
=========
For many years the intersection of computing and data analysis contained
menu-based statistics packages and not much else. Recently, statisticians
have embraced computing, computer scientists are using statistical theories
and methods, and researchers in all corners are inventing algorithms to find
structure in vast online datasets. Data analysts now have access to tools
for exploratory data analysis, decision tree induction, causal induction,
function finding, constructing customised reference distributions, and
visualisation. There are prototype intelligent assistants to advise on
matters of design and analysis. There are tools for traditional, relatively
small samples and for enormous datasets.

The focus of IDA-97 will be 'Reasoning About Data'. We are interested in
intelligent systems that reason about how to analyze data, perhaps as human
analysts do. Analysts often bring exogenous knowledge about data to bear
when they decide how to analyze it; they use intermediate results to decide
how to proceed; they reason about how much analysis the data will actually
support; they consider which methods will be most informative; they decide
which aspects of a model are most uncertain and focus attention there; they
sometimes have the luxury of collecting more data, and plan to do so
efficiently. In short, there is a strategic aspect to data analysis, beyond
the tactical choice of this or that test, visualisation or variable.

Topics
======
The following topics are of particular interest to IDA-97:

* APPLICATIONS & TOOLS

- analysis of different kinds of data (e.g., censored, temporal etc)
- applications (e.g., commerce, engineering, finance, legal,
manufacturing, medicine, public policy, science)
- assistants, intelligent agents for data analysis
- evaluation of IDA systems
- human-computer interaction in IDA
- IDA systems and tools
- information extraction, information retrieval

* THEORY & GENERAL PRINCIPLES

- analysis of IDA algorithms
- bias
- classification
- clustering
- data cleaning
- data pre-processing
- experiment design
- model specification, selection, estimation
- reasoning under uncertainty
- search
- statistical strategy
- uncertainty and noise in data

* ALGORITHMS & TECHNIQUES

- Bayesian inference and influence diagrams
- bootstrap and randomization
- causal modeling
- data mining
- decision analysis
- exploratory data analysis
- fuzzy, neural and evolutionary approaches
- knowledge-based analysis
- machine learning
- statistical pattern recognition
- visualization

Submissions
===========
Participants who wish to present a paper are requested to submit a manu-
script, not exceeding 10 single-spaced pages. We strongly encourage that
the manuscript is formatted following the Springer's 'Advice to Authors
for the Preparation of Contributions to LNCS Proceedings' which can be
found on the IDA-97 web page. This submission format is identical to the
one for the final camera-ready copy of accepted papers. In addition, we
request a separate page detailing the paper title, authors' names, postal
and email addresses, phone and fax numbers.

Email submissions in Postscript form are encouraged. Otherwise, five hard
copies of the manuscripts should be submitted.

Submissions should be sent to the IDA-97 Program Chairs:

Central, North and South America: Elsewhere:
Paul Cohen Xiaohui Liu
Department of Computer Science Department of Computer Science
Lederle Graduate Research Center Birkbeck College
University of Massachusetts, Amherst University of London
Amherst, MA 01003-4610 Malet Street
USA London WC1E 7HX, UK
cohen@cs.umass.edu hui@dcs.bbk.ac.uk

IMPORTANT DATES

February 1st, 1997 Submission of papers
April 15th, 1997 Notification of acceptance
May 15th, 1997 Final camera ready paper

Review
======
All submissions will be reviewed on the basis of relevance, originality,
significance, soundness and clarity. At least two referees will review
each submission independently. Results of the review will be send to the
first author via email, unless requested otherwise.

Publications
============
Papers which are accepted and presented at the conference will appear in
the IDA-97 proceedings, to be published by Springer-Verlag in its Lecture
Notes in Computer Science series. Authors of the best papers will be
invited to extend their papers for further review for a special issue of
'Intelligent Data Analysis: An International Journal'.

IDA-97 Organisation
===================
General Chair: Xiaohui Liu
Program Chairs: Paul Cohen, Xiaohui Liu
Steering Comm. Chair: Paul Cohen, University of Massachusetts, USA
Exhibition Chair: Richard Weber, MIT GmbH, Aachen, Germany
Finance Chair: Sylvie Jami, Birkbeck College, UK
Local Arrangements Chair: Trevor Fenner, Birkbeck College, UK
Public. and Proc. Chair: Michael Berthold, University of Karlsruhe, Germany
Sponsorship Chair: Mihaela Ulieru, Simon Fraser University, Canada

Steering Committee

Michael Berthold University of Karlsruhe, Germany
Fazel Famili National Research Council, Canada
Doug Fisher Vanderbilt University, USA
Alex Gammerman Royal Holloway London, UK
David Hand Open University, UK
Wenling Hsu AT&T Consumer Lab, USA
Xiaohui Liu Birkbeck College, UK
Daryl Pregibon AT&T Research, USA
Evangelos Simoudis IBM Almaden Research, USA

Program Committee

Eric Backer Delft University of Technology, The Netherlands
Riccardo Bellazzi University of Pavia, Italy
Michael Berthold University of Karlsruhe, Germany
Carla Brodley Purdue University, USA
Gongxian Cheng Birkbeck College, UK
Fazel Famili National Research Council, Canada
Julian Faraway University of Michigan, USA
Thomas Feuring WWU Muenster, Germany
Alex Gammerman Royal Holloway London, UK
David Hand The Open University, UK
Rainer Holve Forwiss Erlangen, Germany
Wenling Hsu AT&T Research, USA
Larry Hunter National Library of Medicine, USA
David Jensen University of Massachusetts, USA
Frank Klawonn University of Braunschweig, Germany
David Lubinsky University of Witwatersrand, South Africa
Ramon Lopez de Mantaras Artificial Intelligence Research Institute, Spain
Sylvia Miksch Stanford University, USA
Rob Milne Intelligent Applications Ltd, UK
Gholamreza Nakhaeizadeh Daimler-Benz Forschung und Technik, Germany
Claire Nedellec Universite Paris-Sud, France
Erkki Oja Helsinki University of Technology, Finland
Henri Prade University Paul Sabatier, France
Daryl Pregibon AT&T Research, USA
Peter Ross University of Edinburgh, UK
Steven Roth Carnegie Mellon University, USA
Lorenza Saitta University of Torino, Italy
Peter Selfridge AT&T Research, USA
Rosaria Silipo University of Florence, Italy
Evangelos Simoudis IBM Almaden Research, USA
Derek Sleeman University of Aberdeen, UK
Paul Snow Delphi, USA
Rob St. Amant North Carolina State University, USA
Lionel Tarassenko Oxford University, UK
John Taylor King's College London, UK
Loren Terveen AT&T Research, USA
Hans-Juergen Zimmermann RWTH Aachen, Germany

Enquiries
=========

Detailed information regarding IDA-97 can be found on the World Wide Web
Server of the Department of Computer Science at Birkbeck College, London:

http://web.dcs.bbk.ac.uk/ida97.html

Apart from presentation of research papers, IDA-97 also welcomes demonstr-
ations of software and publications related to intelligent data analysis
and welcomes those organisations who may wish to partly sponsor the confe-
rence.

Relevant enquiries may be sent to appropriate chairs whose details can be
found in the above-mentioned IDA-97 web page, or to

IDA-97 Administrator
Department of Computer Science
Birkbeck College
Malet Street
London WC1E 7HX, UK
E-mail: ida97-enquiry@dcs.bbk.ac.uk
Tel: (+44) 171 631 6722
Fax: (+44) 171 631 6727

There is also a moderated IDA-97 discussion list. To subscribe, send the
word 'subscribe' in the message body to:

ida97-request@dcs.bbk.ac.uk

Previous 11 Next Top

>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
From: Matthias Klusch (mkl@informatik.uni-kiel.de)
Subject: Extended Deadline for CIA-97
Date: Fri, 01 Nov 1996 13:46:18 MEZ

*****************************************************************************
First International Workshop CIA-97

COOPERATIVE INFORMATION AGENTS - DAI meets Database Systems

26th (Wed) - 28th (Fri) of February 1997

University of Kiel, Computer Science Department,
Kiel, Germany
*****************************************************************************

The workshop CIA-97 will be held in cooperation with the research groups on
- Distributed Artificial Intelligence (DAI) FG 1.1.6,
- Database Systems FG 2.5.1, and
- Methods for Information Systems Development (EMISA) FG 2.5.2
of the German Society for Computer Science GI.

&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&

EXTENDED SUBMISSION DEADLINE: 17th of November

&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&
full information:

http://www.informatik.uni-kiel.de/~mkl/cia97.html

Previous 12 Next Top

>~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~