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 dmkdpar@aig.jpl.nasa.gov. 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: kcullen@wkap.com
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 dmkdpar@aig.jpl.nasa.gov and cc-ed to
kcullen@wkap.com. 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 dmkdpar@aig.jpl.nasa.gov.
-------------------------------------------------------------------------------
The dbworld alias reaches many people, and should only be used for
messages of general interest to the database community.
Requests to get on or off dbworld should go to listproc@cs.wisc.edu.
to subscribe send
subscribe dbworld Your Full Name
to unsubscribe send
unsubscribe dbworld
to change your address
send an unsubscribe request from the old address
send a subscribe request from the new address
to find out more options send
help
------------------------------------------------------------------------FOOTER-