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A mutual pruning approach for rknn join processing.
| Content Provider | CiteSeerX |
|---|---|
| Author | Emrich, Tobias Kröger, Peer Niedermayer, Johannes Renz, Matthias Züfle, Andreas |
| Abstract | A reverse k-nearest neighbour (RkNN) query determines the objects from adatabase that have the query as one of their k-nearest neighbors. Processing such a query has received plenty of attention in research. However, the effect of running multiple RkNN queries at once (join) or within a short time interval (bulk/group query) has, to the best of our knowledge, not been addressed so far. In this paper, weanalyze RkNN joins and discuss possible solutions for solving this problem. During our performance analysis we provide evaluation results showing the IO and CPU performance of the compared algorithms for a variety of different setups. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Join Processing Amutual Pruning Approach Bulk Group Query K-nearest Neighbor Different Setup Cpu Performance Possible Solution Performance Analysis Multiple Rknn Query Ashort Time Interval Evaluation Result Areverse K-nearest Neighbour |
| Content Type | Text |