Analysis of predictive spatio-temporal queries Yufei Tao, Jimeng Sun, and Dimitris Papadias   View Paper (PDF)   Return to December 2003, Volume 28, Number 4 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 article, we present probabilistic cost models that estimate the selectivity of spatio-temporal window queriesandjoins,andtheexpecteddistancebetweenaqueryanditsnearestneighbor(s). Ourmodels 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. ©2004 Association for Computing Machinery