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
Indexing the positions of continuously moving objects
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
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
ViEWNet: Visual Exploration of Region-Wide Traffic Networks
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Efficient reverse k-nearest neighbor search in arbitrary metric spaces
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Nearest and reverse nearest neighbor queries for moving objects
The VLDB Journal — The International Journal on Very Large Data Bases
Reverse Nearest Neighbor Search in Metric Spaces
IEEE Transactions on Knowledge and Data Engineering
Reverse kNN search in arbitrary dimensionality
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Incremental Reverse Nearest Neighbor Ranking in Vector Spaces
SSTD '09 Proceedings of the 11th International Symposium on Advances in Spatial and Temporal Databases
Efficiently processing snapshot and continuous reverse k nearest neighbors queries
The VLDB Journal — The International Journal on Very Large Data Bases
DART: an efficient method for direction-aware bichromatic reverse k nearest neighbor queries
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
Hi-index | 0.00 |
In this paper we focus on the problem of continuously monitoring the set of Reverse k-Nearest Neighbors (RkNNs) of a query object in a moving object database using a client server architecture. The RkNN monitoring query computes for a given query object q, the set RkNN(q) of objects having q as one of their k-nearest neighbors for each point in time. In our setting the central server can poll the exact positions of the clients if needed. However in contrast to most existing approaches for this problem we argue that in various applications, the limiting factor is not the computational time needed but the amount of traffic sent via the network. We propose an approach that minimizes the amount of communication between clients and central server by an intelligent approximation of the position of the clients. Additionally we propose several poll heuristics in order to further decrease the communication costs. In the experimental section we show the significant impact of our proposed improvements to our basic algorithm.