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STING+: An Approach to Active Spatial Data Mining
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W. Wang,
J. Yang,, and
R. Muntz
View Paper (PDF)
Return to Session 4: Data Mining I
Spatial data mining presents new challenges due
to the large size of spatial data, the complexity of spatial data types, and
the special nature of spatial access methods. Most research in this area has
focused on efficient query processing of static data. This paper introduces an
active spatial data mining approach which extends the current spatial data
mining algorithms to efficiently support user-defined triggers on dynamically evolving
spatial data. To exploit the locality of the effect of an update and the nature
of spatial data, we employ a hierarchical structure with associated statistical
information at the various levels of the hierarchy and decompose the
user-defined trigger into a set of sub-triggers associated with cells in the
hierarchy. Updates are suspended in the hierarchy until their cumulative effect
might cause the trigger to fire. It is shown that this approach achieves three
orders of magnitude improvement over the naive approach that re-evaluates the
condition over the database for each update, while both approaches produce the
same result without any delay. Moreover, this scheme can support incremental
query processing as well.
Copyright(C) 2000 ACM
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