Leaders
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Jiawei Han, Simon Fraser University
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John Herring, Oracle Corporation
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Harvey Miller, University of Utah
Steering Committee
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Larry Band, University of North Carolina, Chapel Hill
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Max J. Egenhofer, University of Maine
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Suchi Gopal, Boston University
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Richard R. Muntz, University of California, Los Angeles
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John Roddick, University of South Australia Carl
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Stephen Smyth, Microsoft Corporation
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Elizabeth A. Wentz, Arizona State University
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Aidong Zhang, State University of New York at Buffalo
Timeframe: March 18-21, 1999
Location: Microsoft Research Labs, Redmond WA
Description
Digital geographic datasets are growing exponentially and under such activities
as the development of the National Spatial Data Infrastructure, the launching
of new satellite systems with higher resolutions, and the day-to-day collection
of digital imagery, video, and sound. Society has changed from being data-poor
to data-rich, while our techniques for deriving knowledge from the data
in an analytical context have remained inferential in nature. The problem
has now become not finding the data, but filtering through large volumes
of data to finding meaningful geographic knowledge. At the same time, the
types of datasets available are changing from the traditional vector and
raster sets, to include such data types as video and audio, and the location
of where these data were collected. We must overcome these limitations
and develop new approaches and methods that focus upon separating the relevant
from the irrelevant, the meaningful from the background noise. The goal
of this initiative is to find new automated methods for filtering large
amounts of raw geographic data into more user-consumable forms of knowledge.
This includes:
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spatial data mining
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content-based and knowledge-based retrieval
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development of multi-media spatial data types
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on-line analytic processing
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refinement of non-parametric statistics
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incorporation of computational intelligence techniques (such as neural
networks and AI expert systems) into spatial data analysis
