Influence sets based on reverse nearest neighbor queries
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Multidimensional binary search trees used for associative searching
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An Index Structure for Efficient Reverse Nearest Neighbor Queries
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ERkNN: efficient reverse k-nearest neighbors retrieval with local kNN-distance estimation
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Efficient reverse k-nearest neighbor search in arbitrary metric spaces
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IO-Top-k: index-access optimized top-k query processing
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Efficient computation of reverse skyline queries
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Ranked Reverse Nearest Neighbor Search
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NM-Tree: Flexible Approximate Similarity Search in Metric and Non-metric Spaces
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Finding RkNN Straightforwardly with Large Secondary Storage
INGS '08 Proceedings of the 2008 International Workshop on Information-Explosion and Next Generation Search
Reverse k-nearest neighbor search in dynamic and general metric databases
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Nearest Neighbor Retrieval Using Distance-Based Hashing
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
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SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
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DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
Efficient reverse skyline retrieval with arbitrary non-metric similarity measures
Proceedings of the 14th International Conference on Extending Database Technology
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A RkNN query returns all objects whose nearest k neighbors contain the query object. In this paper, we consider RkNN query processing in the case where the distances between attribute values are not necessarily metric. Dissimilarities between objects could then be a monotonic aggregate of dissimilarities between their values, such aggregation functions being specified at query time. We outline real world cases that motivate RkNN processing in such scenarios. We consider the AL-Tree index and its applicability in RkNN query processing. We develop an approach that exploits the group level reasoning enabled by the AL-Tree in RkNN processing. We evaluate our approach against a Naive approach that performs sequential scans on contiguous data and an improved block-based approach that we provide. We use real-world datasets and synthetic data with varying characteristics for our experiments. This extensive empirical evaluation shows that our approach is better than existing methods in terms of computational and disk access costs, leading to significantly better response times.