Indexing the positions of continuously moving objects
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Time-parameterized queries in spatio-temporal databases
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Nearest Neighbor and Reverse Nearest Neighbor Queries for Moving Objects
IDEAS '02 Proceedings of the 2002 International Symposium on Database Engineering & Applications
K-Nearest Neighbor Search for Moving Query Point
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Monitoring k-Nearest Neighbor Queries over Moving Objects
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
SEA-CNN: Scalable Processing of Continuous K-Nearest Neighbor Queries in Spatio-temporal Databases
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Conceptual partitioning: an efficient method for continuous nearest neighbor monitoring
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Proceedings of the 6th international conference on Mobile data management
Nearest and reverse nearest neighbor queries for moving objects
The VLDB Journal — The International Journal on Very Large Data Bases
Continuous nearest neighbor search
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Continuous K-nearest neighbor queries for continuously moving points with updates
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Hi-index | 12.05 |
Continuous K-nearest neighbor (CKNN) query is an important type of spatio-temporal queries. Given a time interval [t"s,t"e] and a moving query object q, a CKNN query is to find the K-nearest neighbors (KNNs) of q at each time instant within [t"s,t"e]. In this paper, we focus on the issue of scalable processing of CKNN queries over moving objects with uncertain velocity. Due to the large amount of CKNN queries that need to be evaluated concurrently, efficiently processing such queries inevitably becomes more complicated. We propose an index structure, namely the CI-tree, to predetermine and organize the candidates for each query issued by the user from anywhere and anytime. When the CKNN queries are evaluated, their corresponding candidates can be rapidly retrieved by traversing the CI-tree so that the processing time is greatly reduced. A comprehensive set of experiments is performed to demonstrate the effectiveness and the efficiency of the CI-tree.