Subject: Call for Papers: Data Mining and Knowledge Discovery (2nd post)
From: [email-masked] (Christopher Dean)
Date: 12 Mar 1997 22:28:24 GMT
Newsgroups: comp.ai,comp.ai.edu,comp.ai.neural-nets,comp.ai.fuzzy,comp.ai.genetic,comp.edu,comp.org.acm,comp.human-factors,comp.org.ieee,ieee.announce,comp.infosystems.gis,comp.graphics.visualization,comp.databases,comp.databases.olap,comp.databases.rdb,sci.stat.edu

[Call for papers on Data Mining and Knowledge Discovery.]

		  DATA MINING AND KNOWLEDGE DISCOVERY 

			   Special Issue on 
	      Scalable High-Performance Computing for KDD

	       Guest editors: Paul Stolorz and Ron Musick
	       ==========================================

    	http://www.research.microsoft.com/research/datamine/dmkdpar

    Traditional computational techniques and computer architectures
    are routinely overwhelmed by the sheer volume and complexity of
    information generated from data-gathering instruments,
    computational and experimental methodologies, and business
    operations.  The fundamental problem of extracting knowledge and
    insight from massive databases and datasets is shared across a
    wide range of fields in business, academia and government. The new
    field of Data Mining and Knowledge Discovery in Databases (KDD)
    has arisen as an interdisciplinary response to this situation,
    merging ideas drawn from disciplines such as statistics, pattern
    recognition, machine learning, databases, visualization and high
    performance computing.

    This special issue of Data Mining and Knowledge Discovery is
    devoted to the challenge of applying data mining and knowledge
    discovery methods to large, complex datasets. Implementation of
    data mining ideas in high-performance computing environments is
    crucial for coping with large-scale data.  In particular, parallel
    and distributed systems are needed to ensure system scalability as
    datasets grow inexorably in size and scope. These environments
    include dedicated massively parallel supercomputers, super-servers
    built from clusters of commodity workstations and high-speed
    network interfaces, and heterogeneous networks distributed over
    regional, national and global scales.  High-performance and
    parallel computing holds the promise of scaling to large data
    sets, allowing the data mining component to search a much larger
    set of patterns and models than traditional computational
    platforms and algorithms would allow. In addition, it promises to
    render the KDD process much more interactive by allowing fast
    response times for difficult search and model fitting problems.

    Data Mining and Knowledge Discovery, published by Kluwer Academic
    publishers, is the flagship publication in the rapidly growing
    area of KDD.  In this special issue we solicit the most dramatic
    new developments in high performance large-scale KDD applications,
    highlighting the promise of the technology and identifying the
    main challenges for the future.  Technically innovative papers
    that describe new theoretical developments, or tackle the
    application of practical data mining approaches to real problems
    and datasets on parallel and distributed architectures, are
    solicited. Topics of interest include, but are not limited to, the
    intersection of KDD with the following fields:

    Parallel implementations of datamining & KDD methods:
        Classification and regression: e.g. decision trees, neural nets
        Pattern recognition
        Belief nets and other Bayesian approaches
        Genetic programming 
        Association rules
        Statistical inference
        Similarity detection and measurement
        Clustering and density estimation
        Change-detection
        Text retrieval
        Content-based indexing
        Data visualization
        Trend Analysis

    Integration of KDD techniques with scalable I/O systems:
	Data warehouses & federated databases        
        Parallel file systems
        High-performance network interfaces
        Intelligent data layout
        Out-of-core algorithms
        Parallel relational querying
        High performance storage systems
        Hierarchical and distributed storage

    Methods to control complexity:
        Random sampling
        Anytime algorithms applied to datamining techniques
        New complex data-type algorithms (eg. not based on feature vectors)
        Domain simplification techniques
        Inference error/confidence characterization

    Parallel, clustered and/or distributed applications:
        Datamining on commodity-based clusters and networks
        Web-oriented datamining
        Novel applications and case studies
        Knowledge discovery systems and tools

    SCOPE AND REVIEW CRITERIA
    Articles are solicited that deal with both theoretic and
    application- oriented approaches to handling the problems inherent
    in large-scale KDD.  All submitted articles should be relevant to
    KDD, clearly indicating which aspect of large-scale KDD is being
    addressed.  Papers should be clearly written and accessible to
    readers from several disciplines.  A well-written, motivated
    introduction is especially important.  Assumptions and limitations
    of the methods described must be discussed.  Contributions must
    represent either a fundamental advance in algorithms and methods,
    or a novel application with clear roots in systematic principals.
    The scaling properties of algorithms and architectures with
    respect to problem size and complexity must be discussed, and
    where appropriate analysis of the throughput and latencies of the
    systems described.

    In addition to full-length papers (see below), short application
    summaries (1-3 pages) are also encouraged.  All submissions will
    be reviewed on the basis of relevance, originality, significance,
    soundness and clarity.  At least three referees will review each
    submission independently. Results of the review will be sent to
    the first author via email, unless otherwise requested.

    SUBMISSION INSTRUCTIONS
    Electronic submissions are STRONGLY ENCOURAGED. Postscript copies
    of papers may be emailed to [email-masked]. Latex style
    files and related instructions can be obtained at the web site
    http://www.research.microsoft.com/research/datamine.

    Submissions of full papers should be limited to at most 28 pages in
    12pt font, 1.5 line-spacing. Electronic submissions will speed the
    review process significantly, however due to Kluwer requirements,
    authors must also submit hardcopy papers. All authors must submit
    (6) hardcopy papers as follows:

	five (5) hardcopies to:

   	Ms. Karen Cullen,
    	DATA MINING AND KNOWLEDGE DISCOVERY
    	Editorial Office, Kluwer Academic Publishers,
    	101 Philip Drive, Norwell, MA  02061
    	phone 617-871-6600  fax 617-871-6528      email: [email-masked]

	one (1) hardcopy to:

	Dr Paul Stolorz
	Attn: DMKD Special Issue 
	MS 525 3660
	Jet Propulsion Laboratory
	4800 Oak Grove Drive 
	Pasadena CA 91109  USA

    In addition, an email message containing title, abstract, and
    keywords must be sent to [email-masked] and cc-ed to
    [email-masked]. Please use the electronic template available on
    the web. For those with no network access, please call Ms. Cullen
    with a request at 617-871-6600.

    The journal emphasizes fast dissemination of results and minimal
    backlogs in publication time. An electronic server will be made
    available by Kluwer containing accepted articles and will be
    accessible by subscribers to the journal. Authors are encouraged
    to make their data available via the journal web site, allowing
    papers to have an "electronic appendix" containing data and
    algorithms.

			   ===============
                           IMPORTANT DATES
			   ===============

                **************************************
                SUBMISSION DEADLINE:       May 8, 1997
                ACCEPTANCE NOTIFICATION: June 20, 1997
                **************************************

    Enquiries about the submission process and scope of the special
    issue may be sent to [email-masked].