The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Influence sets based on reverse nearest neighbor queries
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Progressive approximate aggregate queries with a multi-resolution tree structure
SIGMOD '01 Proceedings of the 2001 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
Efficient OLAP Operations in Spatial Data Warehouses
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
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
Constrained reverse nearest neighbor search on mobile objects
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Boosting spatial pruning: on optimal pruning of MBRs
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Efficient RkNN retrieval with arbitrary non-metric similarity measures
Proceedings of the VLDB Endowment
The inverse classification problem
Journal of Computer Science and Technology
A centroid k-nearest neighbor method
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Reverse spatial and textual k nearest neighbor search
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Top-k most influential locations selection
Proceedings of the 20th ACM international conference on Information and knowledge management
Shared execution strategy for neighbor-based pattern mining requests over streaming windows
ACM Transactions on Database Systems (TODS)
Clustering algorithm based on mutual K-nearest neighbor relationships
Statistical Analysis and Data Mining
Efficiently processing snapshot and continuous reverse k nearest neighbors queries
The VLDB Journal — The International Journal on Very Large Data Bases
Reverse-k-Nearest-Neighbor join processing
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
| Hi-index | 0.01 |
In this paper, we propose an original solution for the general reverse k-nearest neighbor (RkNN) search problem. Compared to the limitations of existing methods for the RkNN search, our approach works on top of any hierarchically organized tree-like index structure and, thus, is applicable to any type of data as long as a metric distance function is defined on the data objects. We will exemplarily show how our approach works on top of the most prevalent index structures for Euclidean and metric data, the R-Tree and the M-Tree, respectively. Our solution is applicable for arbitrary values of k and can also be applied in dynamic environments where updates of the database frequently occur. Although being the most general solution for the RkNN problem, our solution outperforms existing methods in terms of query execution times because it exploits different strategies for pruning false drops and identifying true hits as soon as possible.