Yonghun Park
ABSTRACT
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.
- X. Yu, K. Pu, and N. Koudas, Monitoring k-nearest Neighbor Queries over Moving Objects, In Proc. Intl. Conf. Data Engineering, pp.631--642, 2005. Google Scholar
Digital Library
- K. Mouratidis, M. Hadjieleftheriou, and D. Papadias, Conceptual Partitioning: An Efficient Method for Continuous Nearest Neighbor Monitoring, In Proc. ACM Conf. Management of Data, pp. 634--645, 2005. Google ScholarDigital Library
- K. Mouratidis and D. Papadias, Continuous Nearest Neighbor Queries over Sliding Windows, IEEE Transactions on Knowledge and data Engineering (TKDE), 19(6), pp.789--803, 2007. Google ScholarDigital Library
- M. A. Cheema, Y. Yuan, and X. Lin, CircularTrip: An Effective Algorithm for Continuous kNN Queries, In Proc, Intl. Conf. Database systems for Advanced Applications (DASFAA), 2007. Google ScholarDigital Library
Index Terms
- k-Nearest neighbor query processing method based on distance relation pattern
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