OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 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
An Index Structure for Efficient Reverse Nearest Neighbor Queries
Proceedings of the 17th International Conference on Data Engineering
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
All-Nearest-Neighbors Queries in Spatial Databases
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
Outlier Detection Using k-Nearest Neighbour Graph
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
The k-Nearest Neighbour Join: Turbo Charging the KDD Process
Knowledge and Information Systems
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 Nearest Neighbor Search in Metric Spaces
IEEE Transactions on Knowledge and Data Engineering
Clustering Using a Similarity Measure Based on Shared Near Neighbors
IEEE Transactions on Computers
Reverse kNN search in arbitrary dimensionality
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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
Reverse k-Nearest Neighbor Search Based on Aggregate Point Access Methods
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
High-dimensional kNN joins with incremental updates
Geoinformatica
Boosting spatial pruning: on optimal pruning of MBRs
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
On the impact of flash SSDs on spatial indexing
Proceedings of the Sixth International Workshop on Data Management on New Hardware
Influence zone: Efficiently processing reverse k nearest neighbors queries
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Inverse queries for multidimensional spaces
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
Spatial outlier detection: data, algorithms, visualizations
SSTD'11 Proceedings of the 12th international conference on Advances in spatial and temporal databases
Ranking outliers using symmetric neighborhood relationship
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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A reverse k-nearest neighbour (RkNN) query determines the objects from a database that have the query as one of their k-nearest neighbors. Processing such a query has received plenty of attention in research. However, the effect of running multiple RkNN queries at once (join) or within a short time interval (bulk/group query) has only received little attention so far. In this paper, we analyze different types of RkNN joins and discuss possible solutions for solving the non-trivial variants of this problem, including self and mutual pruning strategies. The results indicate that even with a moderate number of query objects (|R|≈0.0007|S|), the performance (CPU) of the state-of-the-art mutual pruning based RkNN-queries deteriorates and hence algorithms based on self pruning without precomputation produce better results. During an extensive performance analysis we provide evaluation results showing the IO and CPU performance of the compared algorithms for a wide range of different setups and suggest appropriate query algorithms for specific scenarios.