SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Enhanced nearest neighbour search on the R-tree
ACM SIGMOD Record
The Grid File: An Adaptable, Symmetric Multikey File Structure
ACM Transactions on Database Systems (TODS)
Distance browsing in spatial databases
ACM Transactions on Database Systems (TODS)
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
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
An Index Structure for Efficient Reverse Nearest Neighbor Queries
Proceedings of the 17th International Conference on Data Engineering
VLDB '98 Proceedings of the 24rd 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
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
Multidimensional reverse kNN search
The VLDB Journal — The International Journal on Very Large Data Bases
Reverse kNN search in arbitrary dimensionality
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Image retrieval: Ideas, influences, and trends of the new age
ACM Computing Surveys (CSUR)
FINCH: evaluating reverse k-Nearest-Neighbor queries on location data
Proceedings of the VLDB Endowment
Similarity Search in Arbitrary Subspaces Under Lp-Norm
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Nearest Neighbor Retrieval Using Distance-Based Hashing
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Reverse Furthest Neighbors in Spatial Databases
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Quality and efficiency in high dimensional nearest neighbor search
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
On Index-Free Similarity Search in Metric Spaces
DEXA '09 Proceedings of the 20th International Conference on Database and Expert Systems Applications
Similarity search on Bregman divergence: towards non-metric indexing
Proceedings of the VLDB Endowment
An efficient algorithm for arbitrary reverse furthest neighbor queries
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
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The variants of similarity queries have been widely studied in recent decade, such as k-nearest neighbors (k-NN), range query, reverse nearest neighbors (RNN), an so on. Nowadays, the reverse furthest neighbor (RFN) query is attracting more attention because of its applicability. Given an object set O and a query object q, the RFN query retrieves the objects of O, which take q as their furthest neighbor. Yao et al. proposed R-tree based algorithms to handle the RFN query using Voronoi diagrams and the convex hull property of dataset. However, computing the convex hull and executing range query on R-tree are very expensive on the fly. In this paper, we propose an efficient algorithm for RFN query with metric index. We also adapt the convex hull property to enhance the efficiency, but its computation is not on the fly. We select external pivots to construct metric indexes, and employ the triangle inequality to do efficient pruning by using the metric indexes. Experimental evaluations on both synthetic and real datasets are performed to confirm the efficiency and scalability.