Knowledge Discovery Nuggets 97:16, e-mailed 97-05-08

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Knowledge Discovery Nuggets 97:16, e-mailed 97-05-08

* GPS, first issue of DMKD journal is published!

  • * Gerhard Widmer, CfP: MLJ Special Issue on Context Sensitivity and Concept Drift

  • Siftware:
    * Larry Bouchie, Cognos new Data Mining Tool: Scenario
    * Aleksander Oehrn, Rosetta - rough-set tool for data analysis

  • Positions:
    * Gregory Piatetsky-Shapiro, Data Mining Company looking for
    experts in decision trees and/or bayesian networks
    * Donal Lyons, Data Mining Research Position in Ireland
    * Yike Guo, Data Mining Job at Fujitsu (Japan)
    * Pavel Brazdil, The Workshop on 'Extraction of Knowledge from Data Bases' (EKBD'97), Coimbra, Portugal, October 6-9, 1997

  • * Michael Berthold, IDA-97 Call for Participation

  • * Staal Vinterbo, PKDD'97 Call for participation,
    Trondheim, Norway, June 24-27, 1997,

  • * Rob Tibshirani, Statistical prediction methods for finance and marketing, New York City: June 23-24, 1997,

  • * Angi Voss, Workshop on Social Agents at ECSCW97 Conference
    September 7, 1997

  • --
    Data Mining and Knowledge Discovery community, focusing on the
    latest research and applications.

    Submissions are most welcome and should be emailed, with a
    DESCRIPTIVE subject line (and a URL) to gps.
    Please keep CFP and meetings announcements short and provide
    a URL for details.

    To subscribe, see

  • KD Nuggets frequency is 3-4 times a month.
    Back issues of KD Nuggets, a catalog of data mining tools
    ('Siftware'), pointers to Data Mining Companies, Relevant Websites,
    Meetings, and more is available at Knowledge Discovery Mine site

  • -- Gregory Piatetsky-Shapiro (editor)

    ********************* Official disclaimer ***************************
    All opinions expressed herein are those of the contributors and not
    necessarily of their respective employers (or of KD Nuggets)

    ~~~~~~~~~~~~ Quotable Quote ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
    About the Deep Blue -- Kasparov match,
    'I just think we should look at this as a chess match,' he said, 'between the
    world's greatest chess player and Garry Kasparov.'
    Louis Gerstner, IBM Chairman

    Previous  1 Next   Top
    Date: Thu, 8 May 1997 09:41:10 -0500 (EST)
    From: GPS (gps)
    Subject: First Issue of DMKD journal

    The first issue of DMKD journal has finally been published!

  • The beautiful black and white cover shows an Escher-inspired picture
    of several robots inside a mysterious structure (a data mine?), and
    contents include
    an editorial by Usama Fayyad, 4 excellent technical papers,

    * Statistical Themes and Lessons for Data Mining
    Clark Glymour, David Madigan, Daryl Pregibon, Padhraic Smyth

    * Data Cube: A Relational Aggregation Operator Generalizing Group-by,
    Cross-Tab, and Sub Totals
    Jim Gray, Surajit Chaudhuri, Adam Bosworth, Andrew Layman, Don Reichart, Murali Venkatrao, Frank Pellow, and Hamid Pirahesh

    * On Bias, Variance, 0/1 - loss, and the Curse-of-Dimensionality
    Jerome H. Friedman

    * Bayesian Networks for Data Mining, David Heckerman

    and a brief application summary:
    * Advanced Scout: Data Mining and Knowledge Discovery in NBA data,
    Inderpal Bhandari, Ed Colet, Jennifer Parker, Zachary Pines, Rajiv Pratap, Krishnakumar Ramanujam
    Sample copies of first issue will be mailed soon.

    Previous  2 Next   Top
    Date: Wed, 30 Apr 1997 11:09:50 +0200 (MET DST)
    From: Gerhard Widmer (
    Subject: CfP: MLJ Special Issue on Context Sensitivity and Concept Drift

    Machine Learning Journal
    Special Issue on Context Sensitivity and Concept Drift
    Miroslav Kubat and Gerhard Widmer, Guest Editors


    In many machine learning applications, the features given to the
    learning program do not capture all aspects of the application problem.
    This is a limitation shared with all forms of modeling -- even the
    person who formulates the learning problem may not be aware of all of
    the relevant context. Examples from the history of machine learning
    and pattern recognition include omitting illumination features in
    computer vision and omitting language accents in speech recognition
    systems. A similar problem arises when the relevant features are
    included, but the training examples do not provide enough variation
    of those features to permit the learning algorithm to detect their
    relevance. For example, if foreign accent features are included in a
    speech recognition system, but all training examples are from native
    speakers, then the foreign accent features will be ignored by the
    learning system.

    Relevant context may also change with time, so that a classifier
    trained on one set of training examples (where a contextual feature
    was absent or held constant) may suddenly begin to perform badly when
    the context changes. Gradual or abrupt changes in context often
    become apparent in the form of {em concept drift}. For situations
    where a concept gradually evolves over time in a certain general
    direction (such as the concept ``computer''), the term {em concept
    evolution} has sometimes been used. Tracking concept drift on-line
    requires a learner to continually monitor its performance and adjust
    its hypotheses if necessary. It might also require the learner to
    'forget' old, outdated information.

    In batch learning, problems may arise if the training data were
    collected in batches from different contexts, or if the training
    data were gathered in one setting but the test data are drawn from
    a different setting. Again, effective learning requires the recognition
    of such discontinuities and the ability to adapt hypotheses to
    different conditions.

    This special issue is devoted to theoretical and empirical studies
    of methods for detecting missing context, tracking concept drift,
    adapting learned knowledge to new contexts, and identifying and
    reasoning about contextual effects and concept changes in learning.
    We encourage submissions addressing one or more of the following
    research issues:

    . on-line tracking of concept drift and concept evolution
    . theoretical results concerning concept drift and contextual influences
    . formal definitions of context and its effects on concept learning
    . real-world applications involving context changes and/or concept drift
    . representation of context-sensitive concepts
    . representation of context
    . recognition of context and reasoning about context
    . adaptation of learned knowledge to new contexts

    Both theoretical and more practically oriented papers are welcome,
    but we do encourage papers that provide real-world examples of context
    sensitivity and concept drift and compare multiple ways of addressing
    the problems that arise.


    The expected length is 8000-12000 words for a full paper, or 2000-4000
    words for a Research Note (full-page figures count for 400 words).
    Electronic submission via e-mail is STRONGLY ENCOURAGED. Postscript
    files (compressed or gzipped, uuencoded) should be sent to

    For hardcopy submissions, please send 5 copies of the manuscript to:

    Gerhard Widmer
    Austrian Research Institute for Artificial Intelligence
    Schottengasse 3
    A-1010 Vienna
    Tel: +43-1-53532810
    Fax: +43-1-5320652

    The submission deadline is September 15, 1997.

  • for full details.

    The special issue is scheduled to appear in the summer of 1998.

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    Date: Mon, 28 Apr 1997 13:38:14 +0200
    To: gps
    From: Aleksander Oehrn (
    Subject: Rosetta availability

    Rosetta -- A Rough Set Toolkit for Analysis of Data

    Rosetta is a toolkit for analyzing tabular data within the framework of
    rough set theory, and consists of a computational kernel and a GUI
    front-end. The Rosetta GUI reflects the contents of the kernel, and runs on
    PCs operating under Windows NT or Windows 95.

    A limited version of Rosetta is made publicly available for non-commercial
    use. The downloadable program is limited in the sense that algorithms from
    the embedded RSES library are not applicable to decision tables larger than
    some predetermined size (currently 500 objects and 20 attributes).


  • The software (including documentation) is provided 'as is' without warranty
    of any kind.

    Kernel architecture and front-end designed and implemented at the Knowledge
    Systems Group, Dept. of Computer and Information Science, Norwegian
    University of Science and Technology, Norway. Sections of the computational
    kernel (RSES) developed at the Logic Group, Inst. of Mathematics,
    University of Warsaw, Poland.

    Rosetta is designed to support the overall KDD process; from initial
    browsing and preprocessing of the data, via reduct computation and rule
    generation, to validation and analysis of the extracted rules.

    Some of the features currently offered by the computational kernel include
    amongst others:

    - Completion of decision tables with missing values
    according to various completion strategies.
    - Computation of partitions and rough set approximations
    within the variable precision model.
    - Sampling of subtables for validation purposes.
    - Discretization of numerical attributes with various
    discretization algorithms.
    - Computation of reducts (both in the standard sense as well
    as object-related ones). Various approximation algorithms
    (e.g. genetic algorithms) are offered, as well as exhaustive
    computation via discernibility matrices. Dynamic reducts can
    be computed.
    - Generation of propositional rules.
    - Shortening and pruning of sets of reducts and rules.
    - Exporting of rules, reducts and tables, e.g. to Prolog.
    - Application of synthesized rules to unseen examples by means
    of various classification strategies, e.g. voting.
    - Generation of confusion matrices.

    Some of the features currently offered by the Rosetta GUI include amongst

    - Full Windows GUI conformance.
    - Organization of project items in a tree-structure in order to
    retain data-navigational abilities.
    - Viewing of all structures in intuitive grid environments, using
    terms from the modelling domain.
    - Context-sensitive menus.
    - Drag and drop functionality.
    - Masking of attributes, enabling one to work with 'virtual'
    - Automatic generation of annotations, thus documenting the
    modelling session.
    - A prototype environment for interactive classification and guidance
    on the basis of incomplete information, using a selected set of
    synthesized rules.
    - On-line help.

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    Date: Wed, 7 May 1997 17:37:13 -0400
    From: Larry Bouchie (

    Cognos' Scenario data mining product was released
    last month. Cognos' main Web page is at
  • and the
    Scenario site is at

  • Concise background and a review are at

    -- New Data Mining Software Joins Cognos' Market-Leading Business
    Intelligence Tools, PowerPlay' For OLAP And Impromptu' For Query &
    Reporting --

    BURLINGTON, MA, March 3, 1997 -- Cognos (NASDAQ:COGNF; TSE:CSN) today
    announced its newest business intelligence tool, Scenario, for
    enterprise-wide guided data analysis and data mining. Scenario extends the
    industry's most comprehensive business intelligence product family, joining
    Cognos' market-leading PowerPlay, the universal online analytical
    processing (OLAP) client, and the award-winning Impromptu query and
    reporting tool.

    Designed for spotting patterns and exceptions in business data that might
    otherwise be missed, Scenario's sophisticated interface allows users to
    readily visualize the business information being uncovered. It automates
    the discovery and ranking of critical factors impacting a business, exposes
    hidden relationships between factors and establishes thresholds and
    benchmarks. An intuitive, cost-effective desktop tool, Scenario liberates
    data mining from what is typically an expensive and time-consuming process.
    Insights derived using Scenario are achieved directly by those best
    positioned to use the knowledge and effect rapid change.

    Designed to support faster business decision-making, Scenario:
    * makes data mining immediately accessible to decision makers;
    * simplifies business data analysis by filtering out insignificant business
    variables and relationships;
    * validates business hypotheses by showing and ranking critical factors and
    * leads to new business insights by automating information discovery; and
    * integrates with Impromptu and PowerPlay as best-of-breed components in
    the Cognos enterprise business intelligence solution.

    'With Scenario, Cognos is delivering a very important technology to
    business analysts,' said George Azrak, national director of IS development
    at Domino's Pizza. Domino's Pizza has been working with early versions of
    Scenario, and has provided Cognos with valuable input from an end user's
    point of view.
    'Accessible data mining is the long-awaited third wave in the data
    warehousing revolution,' said Alan Rottenberg, Cognos' senior vice
    president, Business Intelligence Tools. 'First query and reporting brought
    data to the desktop, then OLAP technologies enabled the convenient
    navigation of massive data warehouses. Data mining is the technological
    leap that automates the information discovery process.

    Rottenberg continued, 'Impromptu gives access to the numbers and data on
    which a business runs. PowerPlay lets individual managers explore that
    data without an army of programmers. Scenario works alongside both of
    those products to refine business data to distinguish what really matters.
    Drawing a straight line to the bottom line, this product completes the
    spectrum of business intelligence tools that can arm knowledge workers with
    the insight to truly understand the data that drives a business -- and to
    reap the competitive rewards.'

    Scenario uses statistical methods that go beyond 'tree' analysis. For
    example, one such method is a data segmentation capability based on CHAID
    (Chi-Squared Automatic Interaction Detection) technology. CHAID allows
    users to find statistically relevant relationships and trends within large
    repositories of business data by 'refining' it down to the most useful
    nuggets that have the greatest effect on the results being tracked.
    Subsequent releases of Scenario will include neural-network modeling and
    forecasting capabilities, using technologies from recently acquired Right
    Information Systems.

    Pricing and Availability
    Available from Cognos for $695, Scenario 1.0 for Windows 95 or Windows NT
    requires an IBM-compatible 486 PC and 8 MB of RAM.

    Previous  5 Next   Top
    Date: Thu, 8 May 1997 10:40:10 -0500 (EST)
    From: Gregory Piatetsky-Shapiro (
    Subject: Looking for experts in decision trees and/or bayesian networks

    ** Data Mining Consulting and Integration Company is looking for
    experts in decision trees and/or bayesian networks **

    TASK: Participate in the design, development, and deployment of leading
    edge integrated data mining and customer modeling systems, primarily in
    the financial area. Perform quick data mining studies using a variety of
    different approaches and tools.

    The candidates will join a team of world-class experts in data
    warehousing, data mining and knowledge discovery.

    Ideal candidates will have a Ph.D. in Machine Learning, Statistics,
    or related fields and 2-3 years of experience, or an M.S. with an
    equivalent experience. The candidates should have expertise with
    different modeling approaches, but primarily
    with with decision trees/rules or with bayesian belief networks.
    The candidates should be familiar with statistical theory and have practical
    experience with databases.

    Excellent coding skills in C/Java/Unix environment along with
    good system maintenance practices and the ability to
    quickly pick up new systems and languages are needed.
    The candidates should also have good communication skills, be
    able to work in a team, and be able to enjoy the exciting atmosphere of
    a start-up company.

    Most of all, candidates should have the passion for developing and
    applying innovative methods for solving practical problems.

    We offer very competitive salaries, and our outstanding benefits include
    profit sharing, stock options, medical/dental insurance, and a 401(k)

    The data mining branch of the company is conveniently located in the
    Cambridge area, easily accessible by public transportation.

    Proper work authorization required.

    Please email your resume and a cover letter (in plain ASCII, please) to:

    Gregory Piatetsky-Shapiro, Ph.D.
    Director of Applied Research
    Geneve Consulting Group
    545 Concord Ave
    Cambridge MA 02138
    tel: 617-661-1358
    fax: 617-491-4936

  • Previous  6 Next   Top
    Subject: Data Mining Research Position possibility.
    Date: Sat, 26 Apr 1997 11:57:24 +0100
    From: Donal Lyons (

    Currently there is EU funding available for experienced researchers to
    spend a year in countries such as Ireland. I wish to explore the
    possibility of using this funding to help develop a Data Mining Interest
    Group within the School of Systems and Data Studies in Trinity College,

    I'd like to discuss this further with any experienced EU researchers who
    are at least tentatively interested.


    Donal Lyons, Phone (1000-1700 GMT) +353 1 608 1919
    Lecturer (Information Systems) Phone Messages +353 1 608 1767
    School of Systems & Data Studies
    Trinity College, Dublin 2, FAX on request

    Previous  7 Next   Top
    Date: Mon, 5 May 97 11:48 BST
    From: Yike Guo (
    Subject: Job in Japan

    A Fujitsu subsidiary company which is developing OLAP and datamining tools
    is now looking for a foreign engineer who is interested in working in Japan.

    Carrier opportunity for a programing engineer in Japan

    Designing and programing data mining products which include
    a visualizing OLAP client.

    - BS or MS degree related to computer science
    - C programming skill (VC++ on NT background is best)
    - Familiarity with datamining, visualization, or OLAP
    - Native English speaker

    Fujitsu SWE, Manager Mr. Katoh

    Previous  8 Next   Top
    Date: Tue, 29 Apr 1997 19:30:03 +0200 (MET DST)
    From: Pavel Brazdil (

    Call for Participation
    The Workshop on 'Extraction of Knowledge from Data Bases'

  • Under the auspices of the
    Portuguese Conference on Artificial Intelligence (EPIA'97) Coimbra,
    Portugal, October 6-9, 1997

    October, 7-8, 1997
    Coimbra University Physics Building

    Aims of the Workshop
    This workshop is in the area of Extraction (or Discovery) of Knowledge from
    Data Bases and Data Mining, which are rather recent but expanding
    rapidly. The objective of the workshop is to discuss methods for non-trivial
    extraction of information which is implicit in the existing data and which
    can be represented in a high-level language so as to facilitate interpretation.
    EKBD'97 welcome original papers in English on the following topics:

    - Machine Learning methods useful in KDD and Data Mining,
    (decision tree /rule induction, relational learning (ILP) etc.)

    - Statistical methods useful in KDD and Data Mining,
    (multivariate analysis, principle components, clustering, regression
    methods etc.),

    - Reduction of complexity through preprocessing,
    (identification of relevant attributes, data sampling, clustering, etc.),

    - Data summarization and consolidation,
    - Languages useful in describing user's hypotheses,
    - Applications of KDD and Data Mining,
    - other related areas of interest.

    Workshop Format and Attendance Requirements:
    The workshop will include invited talks, paper presentations and a panel
    discussion. The workshop will last 1-2 days.

    Papers in English, with no more than 15 pages are welcome.
    Attendees should be registred to the main EPIA conference.

  • Submit 3 copies of the full paper to the address below:
    Pavel Brazdil
    LIACC, Universidade do Porto,
    R. Campo Alegre, 823,

    Text format should follow Springer Verlag Lecture Notes Series.
    English is the official language of the workshop.

    Important dates:
    June, 16: submissions due
    July, 15: notifications sent
    September, 8: final versions due

    Programme Committee:
    Pavel Brazdil, Univ.Porto (chair)
    Arlindo Oliveira, IST
    Carlos Bento, U. Coimbra
    Ernesto Costa, U. Coimbra
    Fernando Moura-Pires, UNL-FCT
    Fernando Nicolau, UNL-FCT
    Helena Bacelar Nicolau, UNL-FCT
    Joaquim Pinto da Costa, Univ. Porto
    Paulo Azevedo, Univ. Minho
    Paula Brito, Univ. Porto
    Paulo Gomes, INE, Porto

    Organizing Committee:
    Pavel Brazdil (chair)
    LIACC, Universidade do Porto, R. Campo Alegre, 823,
    Tel.: (02) 600 1672, Fax: (02) 600 3654

    Fernando Moura-Pires
    UNL-FCT, Dept. Informatica, Quinta da Torre
    2825 Monte da Caparica, PORTUGAL
    Tel.: (01) 295 4464, Fax: (01) 295 5641

    Previous  9 Next   Top
    Subject: IDA Call for Participation
    Date: Thu, 8 May 1997 17:43:12 +0200
    From: Michael Berthold (


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

    In Cooperation with

  • ]

    You are invited to participate in IDA-97, to be held in the heart of London.
    IDA-97 will be a single-track conference consisting of oral and poster
    presentations, invited speakers, demonstrations and exhibitions. The
    conference Call for Papers introduced a theme, 'Reasoning About Data',
    and many papers complement this theme, but other, exciting topics have emerged,
    including exploratory data analysis, data quality, knowledge discovery and
    data-analysis tools, as well as the perennial technologies of classification
    and soft computing. A new and exciting theme involves analyzing time series
    data from physical systems, such as medical instruments, environmental data
    and industrial processes.

    Information regarding registration as well as the preliminary technical
    program can be found on the IDA-97 web page (address listed above). Please
    note that there are reduced rates for early registration (before 2nd June).
    Also there are still a limited number of spaces available for exhibition,
    and potential exhibitors are encouraged to book early (the application
    deadline is 2nd June).

    Previous  10 Next   Top
    From: 'Staal Vinterbo' (
    Message-Id: (
    Date: Tue, 6 May 1997 18:05:56 +0200
    X-Mailer: Z-Mail (3.2.1 10oct95)
    Subject: PKDD'97 Call for participation
    Mime-Version: 1.0
    Content-Type: text/plain; charset=us-ascii
    Status: U
    X-Mozilla-Status: 0001
    Content-Length: 4951

    Dear Sir.
    I am asking on behalf of Prof. Komorowski that the following call for
    participation is distributed via the kdd nuggets mailinglist.
    Thank you.

    PKDD'97 -- Call For Participation

    1st European Symposium on Principles of
    Data Mining and Knowledge Discovery
    Trondheim, Norway
    June 24-27, 1997

    Tutorials: June 24-25
    Symposium: June 26-27

    Data Mining and Knowledge Discovery (KDD) have recently emerged from a
    combination of many research areas: databases, statistics, machine
    learning, automated scientific discovery, inductive programming,
    artificial intelligence, visualization, decision science, and high
    performance computing.

    While each of these areas can contribute in specific ways, KDD focuses on
    the value that is added by creative combination of the contributing areas.
    The goal of PKDD'97 is to provide a European-based forum for interaction
    among all theoreticians and practitioners interested in data mining.
    Fostering an interdisciplinary collaboration is one desired outcome, but
    the main long-term focus is on theoretical principles for the emerging
    discipline of KDD, especially those new principles that go beyond each of
    the contributing areas.

    Please look at the PKDD'97 Homepage
  • for
    detailed information and news about the symposium.

    Registration Information is available at

  • Previous  11 Next   Top
    Date: Sun, 4 May 97 12:10 EDT
    Subject: Modern Regression and Classification course - New York

    +++ +++
    +++ Modern Regression and Classification: +++
    +++ +++
    +++ Statistical prediction methods for finance +++
    +++ and marketing +++
    +++ +++
    +++ +++
    +++ New York City: June 23-24, 1997 +++
    +++ +++
    +++ Trevor Hastie, Stanford University +++
    +++ Rob Tibshirani, University of Toronto +++
    +++ +++

    This two-day course will give a detailed overview of statistical models
    for regression and classification. Known as machine-learning in
    computer science and artificial intelligence, and pattern recognition
    in engineering, this is a hot field with powerful applications in
    finance, science and industry.

    This course covers a wide range of models from linear regression
    through various classes of more flexible models to fully nonparametric
    regression models, both for the regression problem and for

    This special version of our popular MRC course is tailored to financial
    and marketing professionals.

    Although a firm theoretical motivation will be presented, the emphasis
    will be on practical applications and implementations, especially in
    the finance and marketing areas. The course will include many examples
    and case studies, and participants should leave the course well-armed
    to tackle real problems with realistic tools. The instructors are at
    the forefront in research in this area.

    After a brief overview of linear regression tools, methods for
    one-dimensional and multi-dimensional smoothing are presented, as well
    as techniques that assume a specific structure for the regression
    function. These include splines, wavelets, additive models, MARS
    (multivariate adaptive regression splines), projection pursuit
    regression, neural networks and regression trees. All of these can be
    adapted to the time-series framework for predicting future trends from
    the past.

    The same hierarchy of techniques is available for classification
    problems. Classical tools such as linear discriminant analysis and
    logistic regression can be enriched to account for nonlinearities and
    interactions. Generalized additive models and flexible discriminant
    analysis, neural networks and radial basis functions, classification
    trees and kernel estimates are all such generalizations. Other
    specialized techniques for classification including nearest- neighbor
    rules and learning vector quantization will also be covered.

    Apart from describing these techniques and their applications to a wide
    range of problems, the course will also cover model selection
    techniques, such as cross-validation and the bootstrap, and diagnostic
    techniques for model assessment.

    Software for these techniques will be illustrated, and a comprehensive
    set of course notes will be provided to each attendee.

    Additional information is available at the Website:


  • Previous  12 Next   Top
    Date: Mon, 05 May 1997 12:45:27 +0200
    From: Angi Voss (
    Subject: Workshop on Social Agents

    'Social Agents in Web-Based CollaborationTS

    at the ECSCWP297 Conference

    September 7, 1997

    Organizers: Thomas Kreifelts, Angi Voss, Gloria Mark, Arnstein Borstad,
    Vidar Hepsoe


    We see signs today that the Web is moving toward an environment where
    new social and collaborative interactions are being realized. Rather
    than continuing to evolve as a single-user environment, the Web is
    beginning to be regarded as an environment where reciprocity and
    awareness of othersP2 activities have an important function. Software
    agents can help develop and support the process of reciprocity by
    helping people find others with similar interests, and helping match
    knowledge to the right people. Agents can also help people collectively
    construct knowledge, shaped around their needs.

    This full-day workshop is intended for designers and researchers from
    academia and industry to discuss the role of agents in dealing with
    social information. How can social agents be integrated into
    collaborative relationships so that information and expertise can be
    distributed and matched to the right people, where appropriate
    relationships can be developed, and where collective knowledge can be

    Participation requires the submission of an input paper (3-6 pages) that
    should try to address the points described above, from any of the
    following aspects:

    -experiences with agent use in collaboration
    -design of agent systems
    -application areas
    -interface design

    The paper should be sent for review by June 15 to:

    Thomas Kreifelts
    D-53754 Sankt Augustin
    Fax: +49-2241-142084

    Electronic submission is encouraged, HTML being the preferred format.
    The selection of participants will be based on the input papers.
    Accepted participants will be notified before the end of June so that
    they can take advantage of early registration by July 1. For those who
    are interested in submitting a paper to the workshop, but are not able
    to meet the June 15 deadline, please contact the organizers as soon as
    possible expressing your interest to participate in the workshop. The
    accepted input papers will be distributed electronically in advance to
    the workshop participants. The workshop will be structured around the
    presentation of selected input papers to stimulate the discussion. Note
    that participation in the workshop requires participation in the ECSCW
    97 conference.

    Important Dates:

    June 15, 1997 - Deadline for submissions

    end of June - Notification of acceptance

    ...July 1, 1997 - Early registration deadline for the ECSCW '97

    September 7, 1997 - The Workshop

    For more information:

  • Angi Voss GMD FIT D-53754 Sankt Augustin
    phone: (+49) 2241-142726
    fax: (+49) 2241-142384

  • Previous  13 Next   Top