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Similarity Join for Low- and High-Dimensional Data (2002)
| Content Provider | CiteSeerX |
|---|---|
| Author | Prabhakar, Sunil Kalashnikov, Dmitri V. |
| Abstract | 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 highdimensional 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. |
| File Format | |
| Publisher Date | 2002-01-01 |
| Access Restriction | Open |
| Subject Keyword | Spatial Join Rsj Algorithm Efficient Processing High-dimensional Data Current Computer Main Memory Disk-based Data Highdimensional Join Large Class New Spatial Join Algorithm Ego Join Similarity Join |
| Content Type | Text |
| Resource Type | Article |
