This paper introduces an effect query algorithm based on TPR tree,such algorithm can support kNN query for moving objects and has a better performance than known algorithms.

An indexing structure-TPR tree(time-parameterized R tree)and basic nearest neighbor queries in static environment are introduced in this paper.

This paper proposes a multidimensional spatial index structure called TPR tree.

Reverse Nearest Neighbor Query Based on TPR-tree in Spatio-temporal Databases;

In order to reduce the complexity of algorithms and enhance the efficiency of TPR-tree,an improved node-split algorithm and a distance-based structure adjusting policy are proposed.

In order to process CKNN queries on the basis of TPR-tree efficiently,a new spatio-temporal distance metrics minmaxdist(t) was presented as a pruning upper bound.

TPR-tree is the most popular indexing method for moving objects,but it is inefficient to deal with a huge number of moving objects within a limited area.

To evaluate large collection of concurrent continuous queries based on TPR-tree and R-tree continuously, propose an incremental processing of continuous queries (ICQ) framework.

Method for Index-building and Dynamic Maintenance of TPR*-tree

TPR*-tree is the most popular indexing method for the current and future position of moving objects,but its frequent updates performance is very low.

In order to speed the query processing,an improved PP-TPR tree was adopted basing on the former analysis of various kinds of spatio-temporal indexing techniques.