Call for Papers - Special Issue on Mining Streaming Data

Information Sciences Journal - Informatics and Computer Science Intelligent Systems Applications
An International Journal, Elsevier

Guest Editors: Jianping Zhang, Huan Liu, and Paul P. Wang

Data mining is increasingly recognized as a key technique to analyzing and understanding the
flood of digital data in many organizations. Data can grow without limit at a high rate of
millions of data items per day. Domains with these continuous data streams include credit
fraud detection, mining e-commerce data, web mining, stock analysis, network intrusion detection,
telecommunication data mining, and counter-terrorism data mining. Mining data streams brings
unique opportunities but also new challenges. The main challenge is that `data-intensive' mining is
constrained by limited resources of time, memory, and sample size. Data mining has traditionally
been performed over static datasets, where data mining algorithms can afford to read the input data
several times. When the source of data items is an open-ended data stream, not all data can be
loaded into the memory and off-line mining with a fixed size dataset is no longer technically feasible
due to the unique features of streaming data.

This special issue is dedicated to Mining Streaming Data. We solicit high-quality, original papers. Papers
should address issues related to mining streaming data, including but not limited to the following topics:

Submissions should be in 12pt font, 1.5 line-spacing, and should not exceed 25 pages.
Submission deadline is March 1, 2004.
Papers in PDF format can be sent to
[email protected] or [email protected] by email. If electronic submission is not possible, please
send five hard copies to the following addresses:

Jianping Zhang
The MITRE Corporation, M/S H305
7515 Colshire Drive
McLean, Virginia 22102-7508
Huan Liu
Department of Computer Science & Engineering
Tempe, AZ 85287-8809