CFP

Distributed and Parallel Databases: An International Journal
Special Issue on PARALLEL AND DISTRIBUTED DATA MINING
URL: www.cs.rpi.edu/~zaki/DPDJ.html

GUEST EDITORS:
Mohammed J. Zaki, Rensselaer Polytechnic Institute (zaki@cs.rpi.edu)
Yi Pan, University of Dayton (pan@cps.udayton.edu)

With the unprecedented growth-rate at which data is being collected
and stored electronically today in almost all fields of human
endeavor, the efficient mining of useful information from the data
available is becoming an increasing scientific challenge and a massive
economic need.  While data mining has its roots in the traditional
fields of machine learning and statistics, the sheer volume of data
today poses the most serious problem.  This special issue on parallel
and distributed data mining methods and processes from both an
algorithmic and systems perspective.

The algorithmic aspects involve the design of efficient, scalable,
disk-based, parallel and distributed algorithms for large-scale data
mining tasks. The challenge is to develop methods that scale to
thousands of attributes and billions of transactions. The techniques
of interest span all major classes of data mining methods such as
association rules, sequences, classification, clustering, deviation
detection, as well as various pre-processing and post-processing
operations like sampling, feature selection, data reduction and
transformation, rule grouping and pruning, exploratory and interactive
browsing, meta-mining, etc.

The systems issues will focus on actual implementation of the
algorithms on a variety of parallel hardware platforms, including
shared-memory systems (SMPs), distributed-memory systems, network of
workstations, hybrid systems consisting of a cluster of SMPs,
geographically distributed systems, etc. The key challenges include
improving the load balancing, improving locality, eliminating false
sharing on SMPs, minimizing synchronization, minimizing communication,
maximizing accuracy of distributed models, integrating heterogeneous
sources, and finding appropriate data layouts. Papers dealing with
intergation of mining with databases and datawarehousing, as well as
successful applications, are also sought.

Authors are encouraged to submit high quality, original work that has
neither appeared in, nor is under consideration by, other journals. We
encourage electronic submission of postscript files. Submissions
should be in 12pt font, 1.5 line-spacing, and should not exceed 30
pages. Send papers ro: zaki@cs.rpi.edu.

IMPORTANT DATES:
Submission   --- January 31st, 2001
Notification --- March 31st, 2001
Camera Ready --- April 30th, 2001