Efficient RkNN Retrieval with Arbitrary Non-Metric Similarity Measures
Summary: RkNN under arbitrary non-metric dissimilarities, where query-time aggregation over attribute distances defines the distance. Uses AL-Tree for group-level reasoning to prune RkNN, beating naive scans and block-based methods on real and synthetic data. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Deepak P
- 2. Prasad M Deshpande
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2 | R-Trees: A Dynamic Index Structure For Spatial Searching | 1984 | SIGMOD | 0.0032118946 |
| 8 | Optimal Aggregation Algorithms for Middleware [Extended Abstract] | 2001 | PODS | 0.0015436578 |
| 91 | M-tree: An Efficient Access Method for Similarity Search in Metric Spaces | 1997 | VLDB | 0.00051785122 |
| 595 | Influence Sets Based on Reverse Nearest Neighbor Queries | 2000 | SIGMOD | 0.00019459302 |
| 1,924 | Reverse kNN Search in Arbitrary Dimensionality | 2004 | VLDB | 0.00010075677 |
| 2,014 | IO-Top-k: Index-access Optimized Top-k Query Processing | 2006 | VLDB | 9.7982231e-05 |
| 2,450 | Efficient Computation of Reverse Skyline Queries | 2007 | VLDB | 8.7855268e-05 |
| 4,035 | Efficient Reverse k-Nearest Neighbor Search in Arbitrary Metric Spaces | 2006 | SIGMOD | 6.5111577e-05 |
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