Subject: DMDK special issue

From: Charles R Musick Jr <[email-masked]>
Date: Tue, 4 Mar 1997 12:41:42 -0800 (PST)

This may be of interest to the metadata miners out there..

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			   CALL FOR PAPERS
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		  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
			   ===============

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                SUBMISSION DEADLINE:       May 8, 1997
                ACCEPTANCE NOTIFICATION: June 20, 1997
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    Enquiries about the submission process and scope of the special issue 
    may be sent to [email-masked].