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
Approximate nearest neighbor queries in fixed dimensions
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
Time-parameterized queries in spatio-temporal databases
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
K-Nearest Neighbor Search for Moving Query Point
SSTD '01 Proceedings of the 7th International Symposium on Advances in Spatial and Temporal Databases
Location-based spatial queries
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Continuous K-Nearest Neighbor Search for Moving Objects
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
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
Approximate continuous K-nearest neighbor queries for uncertain objects in road networks
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Topological operators: a relaxed query processing approach
Geoinformatica
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Continuous K nearest neighbor queries (C-KNN) on moving objects retrieve the K nearest neighbors of all points along a query trajectory. In existing methods, the cost of retrieving the exact C-KNN data set is expensive, particularly in highly dynamic spatio-temporal applications. The cost includes the location updates of the moving objects when the velocities change over time and the number of continuous KNN queries posed by the moving object to the server. In some applications (e.g., finding my nearest taxies while I am moving), obtaining the perfect result set is not necessary. For such applications, we introduce a novel technique, AC-KNN, that approximates the results of the classic C-KNN algorithm, but with efficient updates and while still retaining a competitive accuracy. We evaluate the AC-KNN technique through simulations and compare it with a traditional approach. Experimental results are presented showing the utility of our new approach.