Method and system for performing spatial similarity joins on hig

Data processing: database and file management or data structures – Database design – Data structure types

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707 4, 707101, G06F 1700

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059787945

ABSTRACT:
A method and system are disclosed for performing spatial similarity joins on high-dimensional points that represent data objects of a database. The method comprises the steps of: generating a data structure based on the similarity distance .epsilon. for organizing the high-dimensional points, traversing the data structure to select pairs of leaf nodes from which the high-dimensional points are joined, and joining the points from selected pairs of nodes according to a joining condition based on the similarity distance .epsilon.. An efficient data structure referred to as an .epsilon.-K-D-B tree is disclosed to provide fast access to the high-dimensional points and to minimize system storage requirements. The invention provides algorithms for generating the .epsilon.-K-D-B tree using biased splitting to minimize the number of nodes to be examined during join operations. The traversing step includes joining selected pairs of nodes and also self-joining selected nodes. Alternatively, the data structure is an R+tree generated using biased splitting.

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