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An Efficient Method for Monitoring Continuous Top-k Queries

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Published:04 January 2016Publication History

ABSTRACT

Continuous monitoring of spatial queries has received significant research attention in the past few years. In this paper, we propose an efficient method for continuous monitoring of the top-k queries. In contrast to the conventional top-k query, a new top-k query considers both spatial and non-spatial attributes. We use a grid-based data structure and propose a novel grid access method. The proposed method (i) quickly identifies the moving objects that enter or exit the grid cells and (ii) reduces the number of unnecessary operations by comparing the bit-vector information of moving objects. Experimental simulations demonstrate that the proposed methods are several times faster than the previous methods and use considerably less memory.

References

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  3. Yu, X., Pu, K.Q., Koudas, N. 2005. Monitoring k-nearest neighbor queries over moving objects. In: ICDE, pp. 631--6423 Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Song, Z., Reussopoulos, N. 2001. K-Nearest neighbor search for moving query point. In: Jensen, C.s., Schneider, M., Seeger, B., Tsotras, V.J. (eds.) SSTD 2001. LNCS, vol.2121, pp. 79--96. Springer, Heidelberg Google ScholarGoogle ScholarDigital LibraryDigital Library
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  6. Xiong X., Mokble M., Aref W. 2005. "SEA-CNN: Scalible Processing of Continuous K-Neares Neighbor Queries in Spatiotemporal Databases." Proceedings of ICDE Conference. Google ScholarGoogle ScholarDigital LibraryDigital Library

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  1. An Efficient Method for Monitoring Continuous Top-k Queries

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    • Published in

      cover image ACM Conferences
      IMCOM '16: Proceedings of the 10th International Conference on Ubiquitous Information Management and Communication
      January 2016
      658 pages
      ISBN:9781450341424
      DOI:10.1145/2857546

      Copyright © 2016 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 4 January 2016

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      Overall Acceptance Rate213of621submissions,34%
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