Similarity join for low-and high-dimensional data

The efficient processing of similarity joins is important for a large class of applications. The dimensionality of the data for these applications ranges from low to high. Most existing methods have focussed on the execution of high-dimensional joins over large amounts of disk-based data. The increasing sizes of main memory available on current computers, and the need for efficient processing of spatial joins suggest that spatial joins for a large class of problems can be processed in main memory. In this paper we develop two new spatial join algorithms, the Grid-join and EGO-join, and study their performance in comparison to the state of the art algorithm EGO-join and the RSJ algorithm. Through evaluation we explore the domain of applicability of each algorithm and provide recommendations for the choice of join algorithm depending upon the dimensionality of the data as well as the critical /spl epsiv/ parameter. We also point out the significance of the choice of this parameter for ensuring that the selectivity achieved is reasonable.