Voronoi diagrams—a survey of a fundamental geometric data structure
ACM Computing Surveys (CSUR)
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
A threshold of ln n for approximating set cover
Journal of the ACM (JACM)
Influence sets based on reverse nearest neighbor queries
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 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
M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Discovery of Influence Sets in Frequently Updated Databases
Proceedings of the 27th International Conference on Very Large Data Bases
Nearest Neighbor and Reverse Nearest Neighbor Queries for Moving Objects
IDEAS '02 Proceedings of the 2002 International Symposium on Database Engineering & Applications
High dimensional reverse nearest neighbor queries
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Reverse Nearest Neighbors in Large Graphs
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
ERkNN: efficient reverse k-nearest neighbors retrieval with local kNN-distance estimation
Proceedings of the 14th ACM 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 kNN search in arbitrary dimensionality
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Ranked Reverse Nearest Neighbor Search
IEEE Transactions on Knowledge and Data Engineering
Continuous Reverse Nearest Neighbor Queries on Moving Objects in Road Networks
WAIM '08 Proceedings of the 2008 The Ninth International Conference on Web-Age Information Management
FINCH: evaluating reverse k-Nearest-Neighbor queries on location data
Proceedings of the VLDB Endowment
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
Incremental Reverse Nearest Neighbor Ranking
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Efficient processing of probabilistic reverse nearest neighbor queries over uncertain data
The VLDB Journal — The International Journal on Very Large Data Bases
Incremental Reverse Nearest Neighbor Ranking in Vector Spaces
SSTD '09 Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases
Efficient method for maximizing bichromatic reverse nearest neighbor
Proceedings of the VLDB Endowment
Lazy updates: an efficient technique to continuously monitoring reverse kNN
Proceedings of the VLDB Endowment
VoR-tree: R-trees with Voronoi diagrams for efficient processing of spatial nearest neighbor queries
Proceedings of the VLDB Endowment
Influence zone: Efficiently processing reverse k nearest neighbors queries
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
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A reverse k-nearest neighbor (RkNN) query retrieves the data points which regard the query point as one of their respective k nearest neighbors. A bi-chromatic reverse k-nearest neighbor (BRkNN) query is a variant of the RkNN query, considering two types of data. Given two types of data G and C, a BRkNN query regarding a data point q in G retrieves the data points from C that regard q as one of their respective k-nearest neighbors among the data points in G. Many existing approaches answer either the RkNN query or the BRkNN query. Different from these approaches, in this paper, we make the first attempt to propose a top-n query based on the concept of BRkNN queries, which ranks the data points in G and retrieves the top-n points according to the cardinalities of the corresponding BRkNN answer sets. For efficiently answering this top-n query, we construct the Voronoi Diagram of G to index the data points in G and C. From the information associated with the Voronoi Diagram of G, the upper bound of the cardinality of the BRkNN answer sets for each data point in G can be quickly computed. Moreover, based on an existing approach to answering the RkNN query and the characteristics of the Voronoi Diagram of G, we propose a method to find the candidate region regarding a BRkNN query, which tightens the corresponding search space. Finally, based on the triangle inequality, we propose an efficient refinement algorithm for finding the exact BRkNN answers from the candidate regions. To evaluate our approach on answering the top-n query, it is compared with an approach which applies a state-of-the-art algorithm for answering the BRkNN query to each data point in G. The experiment results reveal that our approach has a much better performance.