SIAM-DM 2006 Workshop on Spatial Data Mining
April 22, 2006
http://www.cs.dartmouth.edu/~cbk/sdm06/

Spatial data sets are at the heart of a variety of scientific and
engineering domains, such as computational fluid dynamics, distributed
sensor and actuator networks, geographical information systems,
parameter studies in computational simulation, structural
bioinformatics, and weather pattern identification.  Rapid advances in
simulation and experimentation in these domains are yielding an
increasing reliance on efficient and effective spatial data mining
algorithms.  These developments demand effective cross-fertilization
and consolidation of computational techniques from fields such as data
mining, qualitative spatial reasoning, scientific computing, and
statistical methodology, in the context of significant applications.
The SIAM-DM 2006 Workshop on Spatial Data Mining aims to provide a
forum for such an exchange.  Example issues that are of topical
interest to the workshop include:

* How can we formally model the behavior of spatial mining algorithms?
* How can we intelligently sample data for `closing-the-loop'?
* What features are pertinent in mining spatial datasets?
* How can we employ spatial reasoning for model inference?
* What software tools aid in robust reasoning about spatial phenomena?

Paper Submission and Review

The workshop seeks to bring together three perspectives on spatial
data mining: application domains with significant spatial data mining
challenges; mining algorithms for modeling and uncovering structures
in spatial data; and sampling algorithms for intelligently collecting
additional data.  In order to focus the discussion at the workshop, we
have formulated a set of challenge problems in one significant
application area: pandemic detection and response.  Spatial data
mining challenges in this area include developing a synthetic
time-varying social network capturing collocation and effective contact
patterns, conducting model-based data aggregation using the derived
network in order to identify the onset of disease and other
qualitative indicators of disease spread, and using the structure of
the network to identify critical individuals and locations for
targeted detection and vaccination.  The workshop website gives more
details.

Papers should be 3-5 pages in SIAM-DM conference format.  A PDF file
should be emailed to the program chairs.  We particularly encourage
submissions responsive to the challenge problems in pandemic
preparedness; relevant work in other application contexts is also
welcome.  A paper can present perspectives on how to address the
challenge problem issues or completed work that addresses these
issues.  Novel work that straddles multiple facets of spatial data
mining, spatial modeling, and active sampling methodology are
especially encouraged.  The proceedings will be published as an
on-line collection of working papers.  Following discussion at the
workshop, participants will be invited to contribute to a summary
report that will be communicated to a premier data mining magazine or
journal.

Important Dates

* Submission: February 27, 2006
* Notification: March 6, 2006
* Workshop: April 22, 2006 (half a day at SIAM-DM 2006)

Organization

The program chairs are Chris Bailey-Kellogg and Naren Ramakrishnan,
and can be contacted at cbk@cs.dartmouth.edu and naren@cs.vt.edu.  The
program committee is as follows:

Chris Bailey-Kellogg, Dartmouth (co-chair)
Jochen Garcke, Australian National University
Carlos Guestrin, Carnegie Mellon University
Jiawei Han, University of Illinois at Urbana-Champaign
George Karypis, University of Minnesota
Tao Li, Florida International University
Madhav Marathe, Virginia Bioinformatics Institute
Naren Ramakrishnan, Virginia Tech (co-chair)
Srinivasan Parthasarathy, The Ohio State University
Shashi Shekhar, University of Minnesota
Feng Zhao, Microsoft Research