MPhil Thesis Defence


"Analysis of Predictive Spatio-Temporal Queries"

By

Mr. Jimeng Sun


Abstract

Given a set of objects S, a spatio-temporal window query q retrieves the
objects of S that will intersect the window during the (future) interval
qT. A nearest neighbor query q retrieves the objects of S closest to q
during qT. Given a threshold d, a spatio-temporal join retrieves the pairs
of objects from two datasets that will come within distance d from each
other during qT. In this paper we present probabilistic cost models that
estimate the selectivity of spatio-temporal window queries and joins, and
the expected distance between a query and its nearest neighbor(s). Our
models capture any query/object mobility combination (moving queries,
moving objects or both) and any data type (points and rectangles) in
arbitrary dimensionality. In addition, we develop specialized
spatio-temporal histograms, which take into account both location and
velocity information, and can be incrementally maintained. Extensive
performance evaluation verifies that the proposed techniques produce
highly accurate estimation on both uniform and non-uniform data.


Date:			Friday, 4 July 2003

Time:			3:00p.m.-5:00p.m.

Venue:			Room 2302
			Lifts 17-18

Committee Members:		Dr. Dimitris Papadias (Supervisor)
			Dr. Sunil Arya (Chairman)
			Dr. Mordecai Golin


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