Influence sets based on reverse nearest neighbor queries
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
An Index Structure for Efficient Reverse Nearest Neighbor Queries
Proceedings of the 17th International Conference on Data Engineering
High dimensional reverse nearest neighbor queries
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Efficient reverse k-nearest neighbor search in arbitrary metric spaces
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Reverse Nearest Neighbor Search in Metric Spaces
IEEE Transactions on Knowledge and Data Engineering
Approximate reverse k-nearest neighbor queries in general metric spaces
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Reverse kNN search in arbitrary dimensionality
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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
Boosting spatial pruning: on optimal pruning of MBRs
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
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In this paper, we formalize the novel concept of Constrained Reverse k-Nearest Neighbor (CRkNN) search on mobile objects (clients) performed at a central server. The CRkNN query computes for a given query object q the set RkNN(q) of objects having q as one of their k-nearest neighbors, iff the result set exceeds a specific threshold m, i.e. Card(RkNN(q)) ≥ m. Otherwise, the query reports an empty result. In our setting, the positions of the query object and database objects are approximated by minimal bounding rectangles that depend on the last reported location of the object, as well as on the time that has been passed since the object reported its recent exact location. We propose an approach that minimizes the amount of communication between clients and central server by using the approximation of the positions to identify true hits and true drops. We present a multi-step filter/refinement framework that uses a novel refinement heuristic to minimize the number of objects that are required to provide their exact location. Our solution does not assume any preprocessing steps which makes it applicable for dynamic environments where updates of the database frequently occur. Experiments show that our approach considerably reduces the communication load compared to existing approaches designed for traditional reverse nearest neighbor search in static data.