CALL FOR PARTICIPATION

Mining Massive Data Sets and Streams: Mathematical Methods and
Algorithms for Homeland Defense

June 17-22, 2002
Princeton, New Jersey
Co-Sponsors:

Center for Communications Research (CCR)
http://www.idaccr.org
and DIMACS http://dimacs.rutgers.edu

Organizers:

Robert Grossman, University of Illinois at Chicago & Two
Cultures
Paul Kantor, Rutgers
Muthu Muthukrishnan, ATT.

For more information: http://dimacs.rutgers.edu/Workshops/Homeland

The amount of data relevant to homeland defense is massive,
distributed and growing rapidly through the addition of high
volume data streams and feeds. This presents fundamentally
new mathematical and statistical challenges. These relate
to: 1) the real time and near real time detection of
significant events in high volume data streams; 2) the
forensic analysis of massive amounts of archived data to
uncover patterns and events of interest; and 3) the mining
of distributed data, which for a variety of reasons will
never be centrally warehoused. To complicate matters
further, homeland defense must concern itself with a variety
of different data types, including, signals, text, images,
transaction data, streaming media, web data, and computer to
computer traffic.

The event will bring together researchers from a variety of
fields for tutorials and specialized talks about these
challenges. The tutorial, which runs from Monday to
Wednesday, will present to non-experts or those wanting a
coherent introduction to the field a variety of tools that
are relevant to the topics described. The workshop, which
runs from Thursday through Saturday, will contain more
specialized talks. It is possible to register for the
tutorial alone, the workshop alone, or both.

There will be tutorials on text mining, parallel data
mining, algorithmic issues in processing data streams,
database support for data mining, on-line learning, forensic
ring analysis, and data fusion, as well as a number of
survey talks.

The workshop will include talks on algorithmic issues in
processing streaming data, text mining & classification,
anomaly detection, outlier analysis, forensic data analysis,
on-line learning, real-time data mining, parallel data
mining, visualization and data mining, and mining graphical
data.

For more information:

http://dimacs.rutgers.edu/Workshops/Homeland