Monitoring k-Nearest Neighbor Queries over Moving Objects
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
Continuous Nearest Neighbor Queries over Sliding Windows
IEEE Transactions on Knowledge and Data Engineering
Circulartrip: an effective algorithm for continuous kNN queries
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
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The k-nearest neighbor (k-NN) query is one of the most important query types for location based services (LBS). Various methods have been proposed to efficiently process the k-NN query. However, most of the existing methods suffer from high computation time and larger memory requirement because they unnecessarily access cells to find the nearest cells on a grid index. In this paper, we propose a new efficient method, called Pattern Based k-NN (PB-kNN) to process the k-NN query. The proposed method uses the patterns of the distance relationships among the cells in a grid index. The basic idea is to normalize the distance relationships as certain patterns. Using this approach, PB-kNN significantly improves the overall performance of the query processing. It is shown through various experiments that our proposed method outperforms the existing methods in terms of query processing time and storage overhead.