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Knowledge Discovery Nuggets(tm) 98:23, e-mailed 98-10-30


News:
  • (text) GPS, Industry News: Informix + Red Brick, JWAVE, Neovista 3.0,
    Clementine 5.0, ACM SIGKDD
  • (text) Maria Zemankova, NSF CISE Next Generation Software Program;
    Due dates: 12/15 letter, 1/12/99 proposal

    Publications:
  • (text) Jud Wolfskill, New Book: Bioinformatics, The Machine Learning Approach
  • (text) John Elder, Comparative papers and KDD-98 tutorial notes available
  • (text) Foster Provost, Incremental Data Mining: brief review and references
  • (text) Stephen Koo, Interview with Professor Gao Wen, 'KDD in China' founder
  • (text) Naren Ramakrishnan, CFP: IEEE Computer Spec. Issue on
    Data Analysis and Mining

    Tools/Services:
  • (text) Anders Bjorvand, Update: Rough Enough Public Domain software,
    based on rough set theory

    Positions:
  • (text) Tom Warden, Menlo Park, CA: Data Mining Analyst at
    Allstate Research Center
  • (text) Padhraic Smyth, Tenure Track Faculty Position at UC Irvine

    Courses:
  • (text) Gabor Por, Virtual Courses: Organizational Intelligence and
    Communities of Practice

    Meetings:
  • (text) Jan Zytkow, CFP: Congress on Evolutionary Computation;
    Special session on data mining
    http://garage.cps.msu.edu/cec99/
  • (text) Lynd Bacon, 1999 AMA Advanced Research Techniques Forum,
    Santa Fe, NM, 6/13/99-6/16/99
    http://www.ama.org/conf/
  • (text) DCI Event, Data Warehouse Summit, Phoenix AZ, December 8-10, 1998
    http://www.dci.com/datawhse
  • (text) Solomon Shimony, CFP: BISFAI-99: Bar-Ilan Symposium on foundations of AI,
    June 23-25, 1999 in Ramat Gan, Israel.
    http://www.cs.biu.ac.il:8080/~bisfai
    --
    Knowledge Discovery Nuggets (tm) is an electronic newsletter focusing
    on the latest news, publications, tools, meetings, and other relevant items
    in the Data Mining and Knowledge Discovery field.
    KD Nuggets is currently reaching over 4800 readers in 65+ countries
    2-3 times a month.

    Items relevant to data mining and knowledge discovery are welcome
    and should be emailed to gps in ASCII text or HTML format.
    An item should have a subject line which clearly describes
    what is it about to KDNuggets readers.
    Please keep calls for papers and meeting announcements
    short (50 lines or less of up to 80-characters), and provide a web site for
    details, such as papers submission guidelines.
    All items may be edited for size.

    To subscribe, see http://www.kdnuggets.com/subscribe.html

    Back issues of KD Nuggets, a catalog of data mining tools
    ('Siftware'), pointers to data mining companies, relevant websites,
    meetings, etc are available at KDNuggets Directory at
    http://www.kdnuggets.com/

    -- Gregory Piatetsky-Shapiro (editor)
    gps

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

    ~~~~~~~~~~~~ Quotable Quote ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
    'It's beautiful up here',
    John Glenn, aboard shuttle Discovery, Oct 29, 1998.

    Previous  1 Next   Top
    Date: Monday, October 19, 1998 11:43 AM
    From: Gregory Piatetsky gps
    Subject: Industry News: Informix + Red Brick, JWAVE, Neovista 3.0,
    Clementine 5.0, SIGKDD

    ***
    Informix took over Red Brick:

    Informix Corporation (NASDAQ:IFMX), the technology leader in
    enterprise database-powered solutions,
    today announced the signing of a definitive agreement to acquire Red Brick Systems,
    Inc. (NASDAQ:REDB), The Data Warehouse Company(R).

    Today's agreement anticipates combining two companies that share a
    common corporate vision of providing open end-to-end solutions for
    customers' decision support needs and a culture of technical
    innovation that solves the most complex data warehousing
    problems. The complementary strengths of Informix and Red Brick will
    provide customers with industry leading decision support solutions
    to gain maximum competitive advantage.

    http://www.pathfinder.com/money/latest/press/BU/1998Oct07/1092.html

    ***
    VISUAL NUMERICS RELEASES JWAVE VERSION 2.0,

    HOUSTON - October 19, 1998 -- Visual Numerics, Inc., a developer of
    computational, visualization and Internet software solutions, today
    released JWAVE Version 2.0, a client/server solution that uses Sun
    Microsystems' Java(tm) components to rapidly develop and deploy
    applications across an enterprise. These JWAVE applications let users
    perform numerical analysis and visual interpretation of large, complex
    data sets. JWAVE's multi-tier architecture is designed to scale with the
    growth of an organization and embraces multiple open standards,
    including Java, JavaScript, HTML, HTTP, TCP/IP, SQL, and ODBC. This
    open architecture also facilitates the integration of third party
    JavaBeans with JWAVE Beans, resulting in faster application development.

    http://www.vni.com/products/wpd/jwave/

    ***
    NeoVista Software Inc. announced release 3.0 of its Decision Series(R)
    software suite.

    Release 3.0 makes building business models easier with its new
    DecisionAssistant, adds three new data mining engines, includes IBM
    AIX platform support, adds individual cases factor analysis to the
    tree induction and neural network mining engines and provides native
    database connectivity for Oracle, Informix, Sybase and DB-2 relational
    databases.

    http://www.neovista.com/Help/WhatsNew.htm

    ***
    ISL released Clementine Version 5.0, ISL's open, best-of-breed data mining tool. New
    additions to the Clementine range will build on an organisation's existing IT
    investment, and allow quick deployment of solutions throughout the enterprise.

    Key Features include:
    * The Clementine External Module Interface - allowing addition of user-specified
    algorithms to be added to Clementine's visual programming environment.

    * Automation of data mining tasks, and embedding data mining components
    in broader solutions, through Batch Execution Mode.

    * Tools for managing data mining projects

    * Presentation of data mining results as web-enabled HTML reports.

    * A link to Microsoft Excel.

    * The first release of the Clementine-SPSS interface, giving access to all
    SPSS statistical facilities, and to complementary data mining tools such as
    AnswerTree.

    For more info, see
    http://www.isl.co.uk/

    ***
    ACM Special Interest Group on Knowledge Discovery and Data Mining
    website is operational at http://www.acm.org/sigkdd/

    Join ACM SIGKDD to participate in the first professional organization
    of data mining and KDD professionals. Membership benefits includes
    discounts on KDD-99 and all future KDD conference registrations,
    subscription to the SIGKDD newsletter, and more ...

    http://www.acm.org/sigkdd/

    Previous  2 Next   Top
    Date: Thu, 15 Oct 1998 10:00:38 -0500
    From: Maria Zemankova mzemanko@nsf.gov
    Subject: NSF CISE Next Generation Software Prog; 12/15 letter, 1/12/99
    proposal

    The following document (nsf998) is now available from the NSF Online
    Document System

    Title: CISE Next Generation Software (NGS) Program
    Type: Program Announcements & Information
    Subtype: Computer/Information Sciences

    (Replaces the Challenges in CISE Program, nsf9762)

    It may be found at:

    http://www.nsf.gov/cgi-bin/getpub?nsf998

    Letter of Intent: December 15, 1998 (recommended, optional)
    PROPOSAL DEADLINE DATE: JANUARY 12, 1999

    NOTE: Relevant to researchers working in information systems, e.g.,
    data-intensive applications and QoS aspects in complex information/software
    systems.

    The Experimental and integrative Activities Division in the Computer and
    Information Science and Engineering (CISE) Directorate announces a new
    thrust: the Next Generation Software (NGS) to support multidisciplinary
    (group-oriented as well as single investigator) research, commencing in
    Fiscal Year 1999.

    The NGS program fosters multidisciplinary software research under two
    components: Technology for Performance Engineered Systems (TPES), and
    Complex Application Design and Support Systems (CADSS). The overall
    thrust of NGS will be research and development for new software
    technologies integrated across the systems' architectural layers, and
    supporting the design and the operation cycle of applications, computing
    and communications systems, and delivering quality of service (QoS). The
    TPES component will support research for methods and tools leading to
    the development of performance frameworks for modeling, measurement,
    analysis, evaluation and prediction of performance of complex computing
    and communications systems, and of the applications executing on such
    systems. The CADSS component will support research on novel software for
    the development and run-time support of complex applications executing
    on complex computing platforms; CADSS fostered technology breaks down
    traditional barriers in existing software components in the application
    development, support and runtime layers, and will leverage TPES
    developed technology for delivering QoS.

    It's expected that technology developed under TPES, when integrated into
    the design process, will lead to substantial decreases in the
    development time and cost of future advanced information systems, from
    the hardware components to the applications executing on such platforms.
    In addition such capabilities, when integrated into the operational
    process of these systems, as envisioned with CADSS, will lower the cost
    of their management, optimize their performance, and ensure QoS.

    The technologies developed will be validated with demonstrations on
    important national interest applications. Multidisciplinary teams will
    involve collaboration among researchers in several areas in computer
    sciences and application developers.

    Previous  3 Next   Top
    Date: Tue, 27 Oct 1998 16:05:10 -0400
    From: Jud Wolfskill wolfskil@MIT.EDU
    Subject: New Book: Bioinformatics, The Machine Learning Approach
    Web: http://mitpress.mit.edu/promotions/books/BALBHS98

    Bioinformatics, The Machine Learning Approach

    Pierre Baldi and S�ren Brunak

    An unprecedented wealth of data is being generated by genome-sequencing
    projects and other experimental efforts to determine the structure and
    function of biological molecules. The demands and opportunities for
    interpreting these data are expanding more than ever. Bioinformatics
    is the development and application of computer methods for analysis,
    interpretation, and prediction, as well as for the design of
    experiments. It has emerged as a strategic frontier between biology and
    computer science.

    Pierre Baldi and S�ren Brunak present the key machine learning
    approaches and apply them to computational problems encountered in the
    analysis of biological data. The book is aimed at two types of
    researchers and students. First are the biologists and biochemists who
    need to understand new data-driven algorithms, such as neural networks
    and hidden Markov models, in the context of biological sequences and
    their molecular structure and function. Second are those with a primary
    background in physics, mathematics, statistics, or computer science who
    need to know more about specific applications in molecular biology.

    Pierre Baldi is Chairman of the Board, Net-ID, Inc. S�ren Brunak is
    Director, Center for Biological Sequence Analysis, The Technical
    University of Denmark.

    Adaptive Computation and Machine Learning series. A Bradford Book

    360 pp., 8 x 9, 62 illus., 10 color
    ISBN 0-262-02442-X
    MIT Press * 5 Cambridge Center * Cambridge, MA 02142 * (617) 625-8569

    Previous  4 Next   Top
    Date: Sat, 24 Oct 1998 23:09:49 -0400
    From: John Elder, elder@datamininglab.com
    Subject: Comparative papers and tutorial notes
    Web: http://www.datamininglab.com

    Comparative papers and tutorial notes now available:

    Updated notes for the recent KDD conference tutorial 'A Comparison of
    Leading Data Mining Tools', by John Elder and Dean Abbott, are now
    available on the Elder Research website: http://www.datamininglab.com

    This popular tutorial provides a comprehensive overview of ten major
    Data Mining software tools currently on the market, and contrasts them
    with seven specialized, less expensive, desktop-level tools. The
    capabilities of each tool are tabulated, and screen shots of exemplary
    implementations of key ideas are displayed.

    On the same ER 'Resources' page, also find two recent IEEE conference papers:

    1) 'An Evaluation of High-end Data Mining Tools for Fraud Detection'
    (Abbott, Matkovsky, & Elder, Oct. 1998), which compares five
    powerful commercial products, and describes the process of tool
    evaluation and selection, and

    2) 'Evaluation of Fourteen Desktop Data Mining Tools' (King, Elder,
    Gomulka, Schmidt, Summers, & Toop, Oct. 1998), which compares and
    contrasts 3-4 products each in the four algorithm categories of
    Neural Networks, Polynomial Networks, Decision Trees, and Rule
    Induction.

    John Elder

    ------------ Data Mining & Pattern Discovery -------------
    John F. Elder IV, PhD ELDER RESEARCH
    elder@datamininglab.com 1006 Wildmere Place
    phone: 804-973-7673 Charlottesville, VA 22901
    fax: 804-995-0064 http://www.datamininglab.com

    Previous  5 Next   Top
    Date: Tue, 20 Oct 1998 08:36:36 -0400
    From: Foster Provost, foster@Basit.COM
    Subject: Incremental Data Mining

    Here're some references to the incremental mining of multiple samples
    in order to scale up decision-tree/rule-set inducers to large
    data sets. Enjoy. -- foster
    ----------------------------------------------------------------------
    Sequential multi-sample techniques have been used by several
    researchers to address learning from large data sets. Quinlan used a
    model-based instance selection approach, called {it windowing}
    cite{quinlan-83}, using $C_{i-1}$ to help select $S_i$. At each
    stage $i$, $S_i$, called the {it window}, is augmented by examples
    that $C_{i-1}$ classifies incorrectly. The selection procedure
    chooses candidate examples either randomly or by stratification. The
    combining procedure simply chooses $C_n$ as the final concept
    description. Catlett studied windowing on several learning problems,
    and found the following cite{catlett-thesis-91}. The effect of
    windowing on learning time varied from problem to problem, from a
    factor of three speedup to a factor of 20 slowdown. Severe slowdowns
    occur when the data are noisy...

    Incremental batch learning approaches cite{clearwater-cheng-1989} are
    hybrids of sampling and incremental learning. Class description
    $C_{i-1}$ is given as ``prior knowledge'' to learning algorithm $L_i$,
    along with subsample $S_i$. The learning algorithm uses $S_i$ to
    evaluate $C_{i-1}$, and uses $C_{i-1}$ as a basis for building $C_i$.
    As with windowing, the combining procedure chooses $C_n$ as the final
    concept description, where $C_n$ was built up across the $n$ learning
    runs.

    Incremental batch learning approaches have been used to scale up to
    example sets that are too large for pure batch processing because of
    limits on main memory, leading to increased accuracy over simple
    sampling cite{provost-buchanan-1995}. Incremental batch learning
    offers speedups because even for learners that, in principle, scale up
    linearly in the number of examples, because if the entire example set
    does not fit in main memory, operating system page thrashing can
    render the learner useless. An incremental batch learning approach
    was used by Domingos to transform an algorithm whose run-time
    complexity is quadratic in the size of the example set to a linear
    algorithm cite{Domingos-RISE-1996}.

    All of these approaches incrementally process samples of instances.
    Similarly, {it feature} samples can be processed iteratively...

    To read more, see:

    Provost F. and Kolluri, V. (1997) 'A Survey of Methods for Scaling
    Up Inductive Learning Algorithms.' Technical Report:
    ISL-97-3, Intelligent Systems Lab., Department of Computer
    Science, University of Pittsburgh. A revised version will
    appear in the journal Data Mining and Knowledge Discovery.
    http://www.pitt.edu/~uxkst/survey-paper.ps

    References

    ibitem{catlett-thesis-91}
    Catlett, J. (1991). Megainduction: machine learning on very large
    databases. {it Ph.D Thesis}, Basser Department of Computer Science,
    University of Sydney, Australia.
    ibitem{clearwater-cheng-1989}
    Clearwater, S.H., Cheng, T.P., Hirsh, H. and Buchanan, B.G. (1989).
    Incremental batch learning. In {it Proc. of the Sixth Intl. Workshop
    on Machine Learning}. San Mateo CA: Morgan Kaufmann. pp: 366-370.
    ibitem{Domingos-RISE-1996}
    Domingos, P. (1996). Efficient Specific-to-General Rule Induction. In {it
    Proc. of the Second Intl.Conf. on Knowledge Discovery and Data Mining
    (KDD'96)}, Menlo Park, CA: AAAI Press, pp: 319-322.
    ibitem{provost-buchanan-1995}
    Provost, F.J. and Buchanan, B.G. (1995). Inductive Policy: The
    pragmatics of bias selection. In {it Machine Learning}, 20, pp:
    35-61.
    ibitem{quinlan-83}
    Quinlan, J.R. (1983). Learning efficient classification procedures
    and their application to chess endgames. In Michalski, R., Carbonell
    J. and Mitchell, T. (eds.) {it Machine Learning: an AI
    approach}. Los Altos, CA. Morgan Kaufmann.

    Previous  6 Next   Top
    Date: Tue, 27 Oct 1998 19:27:00 +0800
    From: Stephen Koo skoo@hkstar.com
    Subject: Interview with 'KDD in China' founder

    I have made an interview with 'KDD in China' founder, Professor Gao Wen.
    'KDD in China' is acted as proxy to exchange research experience
    within China and also outside China. Currently, knowledge discovery
    in database and datamining are one of the hottest academic topics,
    which are supported by Government. ....

    Interested readers may go to my datamining webpage
    http://home.hkstar.com/~skoo/datamine.htm.

    Best regards,
    Stephen Koo.


    Previous  7 Next   Top
    Date: Tue, 20 Oct 1998 12:06:44 -0400
    From: Naren Ramakrishnan naren@cs.vt.edu
    Subject: (CFP) IEEE Computer Special Issue on Data Analysis and Mining

    Call for Papers
    Special Issue of IEEE Computer
    Data Analysis and Mining
    ---------------------------------------------------------------------
    Targeted Publication Date: August 1999

    Recent advances in storage and retrieval of large-scale databases,
    high-speed computing platforms, and fast networks have given a
    significant impetus to developments in datamining, especially in the
    commercial arena. Traditional problems in stock market forecasting,
    marketing, and information retrieval are giving way to diverse
    applications in mineral prospecting, computer-aided design, and
    computational steering. This special issue will focus on emerging
    techniques in data analysis and mining. Specific areas of interest
    include:

    o AI and statistical perspectives on data mining
    o Algorithmic issues and evaluation metrics
    o System issues (storage organization, distributed processing,
    indexing and retrieval mechanisms)
    o Distributed and parallel processing issues
    o Linear algebraic and graph theoretic approaches
    o Current and emerging tools and software
    o Application areas in which data mining has been successfully
    applied

    Guest Editors:
    Ananth Grama, Purdue University, ayg@cs.purdue.edu
    Naren Ramakrishnan, Virginia Polytechnic Institute and State
    University, naren@cs.vt.edu

    Interested authors should submit six copies of manuscripts by
    January 15, 1999 to either of the guest editors. Electronic
    Submission is prefered. Manuscripts should not have been previously
    published or currently submitted for publication elsewhere, and
    should not exceed 6,000 words (Each figure and table amount to 150
    words; References are limited to the most relevant 12).

    Previous  8 Next   Top
    Date: Sun, 25 Oct 1998 22:46:16 +0100
    From: Anders Torvill Bjorvand torvill@trolldata.no
    Subject: Update - Rough Enough Public Domain software, based on rough set theory

    *URL: http://www.trolldata.no/renough/
    *Description: Rough Enough is a tool for experimenting with techniques from
    the Rough Set theory. Data reduction, rule induction and decision support.
    Object Mining is supported as well.
    *Discovery tasks: Classification, Summarization, Deployment.
    *Platform(s): Works on top of Paradox for Windows DBMS
    *Status: Public domain
    *Comments: the present version of Rough Enough is developed under version 7
    of the 4GL DBMS Paradox for Windows from Corel Inc. The code is available
    in both a 16 bit and 32 bit version. I have also a downloadable 32 bit
    version with the runtime version of Paradox 7 included in a single
    installable file.
    *Contact:
    Anders Torvill Bjorvand,
    Troll Data Inc.
    P.O. Box 335
    N-1801 ASKIM
    Norway
    Email: torvill@trolldata.no
    http://www.trolldata.no/torvill/

    Previous  9 Next   Top
    Date: Thu, 29 Oct 1998 08:55:43 -0600
    From: Tom Warden TWARD@allstate.com
    Subject: Menlo Park, CA: Data Mining Analyst at Allstate Research Center

    The Allstate Research Center, located in Menlo Park, CA, has an
    opening for an analyst in its Data Mining Group. Qualified candidates
    must have at least a masters degree in statistics, math, computer
    science, machine learning or a related field. Data mining work
    experience, especially in a business setting, is a big plus. Allstate
    is currently conducting research in the fields of claim fraud and
    marketing. Our team consists of experts from three fields: machine
    learning, insurance research, and data processing. We offer a
    collaborative and supportive work environment, and competitive
    compensation. Inquiries should be sent to:
    Tom Warden, tward@allstate.com. Fax: (650)324-9347.
    Allstate is an Equal Opportunity Employer.

    Tom Warden
    Director, Allstate Research Center

    Previous  10 Next   Top
    Date: Wed, 28 Oct 1998 14:38:21 -0800
    From: Padhraic Smyth smyth@sifnos.ics.uci.edu
    Subject: Tenure Track Faculty Position at UC Irvine

    Dear KDD Colleagues,
    FYI, the tenure-track position advertised below encompasses
    research topics close to machine learning and KDD, namely computational
    statistics and scientific data visualization. I would be grateful
    if you would pass this information along to any of your
    colleagues or students who may be interested. I am happy to answer
    specific questions about the department and UCI, if you
    wish to contact me by email.

    Padhraic Smyth
    Associate Professor
    Information and Computer Science
    University of California, Irvine.
    smyth@ics.uci.edu

    Open Faculty Position in Information and Computer Science at UC Irvine

    The Department of Information and Computer Science (ICS) has a
    tenure-track position open in the general area of interdisciplinary
    applications of computing. Research emphases include areas such as
    computational statistics, scientific data visualization, computer
    graphics and animation, computational biology, medical informatics,
    information organization, storage, retrieval and visualization.

    The available position is at the assistant professor level, but
    exceptional candidates from all ranks will be considered. In all cases,
    we are looking for applicants with a Ph. D. degree in Computer Science
    or a related field, and strong research credentials as evidenced by
    scholarly publications. Applicants for senior positions must also
    demonstrate a proven track record in original research and teaching
    activities.

    ... [edited for space]
    For more information see
    http://www.ics.uci.edu/interfac.html

    Previous  11 Next   Top
    Date: Fri, 16 Oct 1998 18:26:50 -0700 (PDT)
    From: Gabor Por webmaster@co-i-l.com
    Subject: Organizational Intelligence / Communities of Practice

    Knowledge Ecology University invites you to participate in either or both
    of the following two virtual courses:

    1. Increasing an Organization's Intelligence
    http://www.KnowledgeEcology.com/keu/cc/98f3.shtml
    taught by Michael McMaster, November 2-14, 1998

    2. Communities of Practice
    http://www.KnowledgeEcology.com/keu/cc/98f1.shtml
    taught by Etienne Wenger, Ph.D., November 16 to December 1, 1998

    1. In the 'Organizational Intelligence' course you will develop a basic
    understanding of how to:

    * Move from current levels of intelligence to its powerful, self-generating increase
    * Engage the whole organization in removing obstacles to its intelligence
    * Lead an intelligent organization
    * Engage the whole population of an organization in supporting such crucial
    initiatives as communities of practice and generation of knowledge

    2. In the 'Communities of Practice' course you will learn:

    * What communities of practice are and what they are good for
    * How theyhave the potential of becoming part of a knowledge strategy
    * What it takes to foster and sustain them
    * What the fundamental 'infrastructures of knowing' are
    * What it really means to 'manage' knowledge
    * How to address the challenges and opportunities of new technologies

    for more information about courses see web links above.

    Tuition is $450 per course.
    10% discount applies if you register to both courses,
    group discount also available

    Please register at at:
    http://www.KnowledgeEcology.com/keu/reg/index.shtml#form

    George Por
    Founder and Sr. Consultant of Community Intelligence Labs,
    http://www.Co-I-L.com,
    the home of 'knowledge ecology': http://www.KnowledgeEcology.com/

    Previous  12 Next   Top
    Date: Wed, 21 Oct 1998 21:34:35 -0400 (EDT)
    From: Jan M Zytkow zytkow@uncc.edu
    Subject: CFP: Congress on Evolutionary Computation; Special session on data mining
    Web: http://garage.cps.msu.edu/cec99/

    If you are interested in COMBINING KNOWLEDGE DISCOVERY WITH
    EVOLUTIONARY COMPUTATION, please respond to this message. All
    evolutionary approaches to data mining and knowledge discovery from
    data are welcome.

    I am exploring the potential for organizing a SPECIAL SESSION ON DATA
    MINING at the CONGRESS OF EVOLUTIONARY COMPUTATION, (CEC99), to be
    held in Washington DC, July 6-9, 1999 in Mayflower Hotel.

    Please respond immediately, before Nov.1 if you are interested. Send
    me a preliminary title. I may need your abstract soon afterwards.

    If the special session on data mining is approved, your final paper
    will be needed by April 1, 1999.

    All papers accepted for the special session will be included in the
    conference proceedings

    The web page of the conference: http://garage.cps.msu.edu/cec99/

    Looking forward to your response. Best regards,

    -- Jan

    Previous  13 Next   Top
    Date: Mon, 19 Oct 1998 11:01:59 -0500
    From: Lynd D Bacon lynd.bacon@lba.com
    Subject: 1999 AMA Advanced Research Techniques Forum, Santa Fe, NM, 6/13/99-6/16/99
    Web: http://www.ama.org/conf/


    The American Marketing Association will be hosting its 10th
    Advanced Research Techniques (ART) Forum at the El Dorado Hotel in Santa
    Fe NM USA on 6/13/99-6/16/99. The call for participation and an
    invitation from the program chair can be found on the AMA's site at
  • http://www.ama.org/conf/.


    • Suggested topic areas include datamining/knowledge discovery methods and
    applications, data integration and enhancement. The ART Forum is a
    unique conference that provides an opportunity for academics,
    practitioners, and research clients to exchange ideas and solutions.
    Since its inception in 1990, the conference has focused on the use of
    sophisticated methodologies and quantitative techniques in support of
    strategic and tactical decisions.



    Lynd Bacon

    Program Chair


    ---------------------------------

    LBA:Lynd Bacon & Associates, Ltd.

          
  • http://www.lba.com

    •        +1 708 957-0883

           +1 708 957-1920 fax

    ---------------------------------


    Previous  14 Next   Top
    Date: Wednesday, October 21, 1998 8:23 AM
    From: DCI Event Coordinator [netreply@dciexpo.com]
    Subject: Data Warehouse Summit, Phoenix, December 8-10, 1998
    Web: http://www.dci.com/datawhse

    DCI's Data Warehouse Summit just went live! Log onto
    http://www.dci.com/datawhse to see the latest information on DCI's
    Data Warehouse Summit, the comprehensive curriculum for data
    warehousing at the speed of business, December 8-10, 1998 at the
    Phoenix Civic Plaza, Phoenix, Arizona.

    Previous  15 Next   Top
    Date: Fri, 23 Oct 98 12:43:05 EDT
    From: Solomon Shimony (shimony@cs.bgu.ac.il)
    Subject: CFP: BISFAI-99: Bar-Ilan Symposium on foundations of AI,
    June 23-25, 1999 in Ramat Gan, Israel.
    Web: http://www.cs.biu.ac.il:8080/~bisfai

    BAR-ILAN SYMPOSIUM
    ON THE FOUNDATIONS OF ARTIFICIAL INTELLIGENCE
    THEME: BRIDGING THEORY AND PRACTICE

    Bar-Ilan is please to announce its sixth biennial
    Symposium on the Foundations of Artificial Intelligence,
    to be held on June 23-25, 1999 in Ramat Gan, Israel.
    The symposium is international in scope, with invited lectures
    by leading researchers and contributed papers on foundations of AI.
    The invited speakers for BISFAI-99 include
    Stan Rosenschein of Stanford University and
    Leo Joskowicz of Hebrew University.

    We solicit substantial research papers in all areas of Artificial Intelligence,
    including but not limited to,
    automated reasoning machine learning
    data mining natural language processing
    information retrieval planning
    intelligent agents probabilistic reasoning
    knowledge-based systems robotics
    knowledge representation search
    mathematical and philosophical foundations

    We especially encourage papers on the theme of this year's symposium-

    BRIDGING THEORY AND PRACTICE:
    THEORY-BASED PRACTICAL IMPLEMENTATIONS
    AND COMMERCIAL APPLICATIONS

    ... [edited GPS]

    Information on registration, accommodations, and other relevant topics
    will appear in future announcements. You may contact bisfai@cs.ciu.ac.il
    for further information, or visit the BISFAI-99 website at
    http://www.cs.biu.ac.il:8080/~bisfai.
    This site is mirrored in the United States at
    http://www-formal.stanford.edu/leora/bisfai

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