Journal of KIISE:Databases (한국정보과학회논문지:데이타베이스)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
권호 표지

A k-NN Query Processing Method based on Distance Relation Patterns in Moving Object Environments

이동 객체 환경에서 거리 관계 패턴 기반 k-최근접 질의 처리 기법

  • Published : 2009.06.15
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, various methods have been proposed to process k-NN (k-Nearest Neighbors) queries efficiently. However the previous methods have problems that they access additional cells unnecessarily and spend the high computation cost to find the nearest cells. In this paper, to overcome the problems, we propose a new method to process k-NN queries using the patterns of the distance relationship between the cells in a grid. The patterns are composed of the relative coordinates of cells sorted by the distance from certain points. Since the proposed method finds the nearest cells to process k-NN queries with traversing the patterns sequentially, it saves the computation cost. It is shown through the various experiments that out proposed method is much better than the existing method, CPM, in terms of the query processing time and the storage overhead.

최근 유클리드 공간 상에서 효율적인 k-최근접(k-Nearest Neighbors) 질의 처리를 위해 그리드 구조 기반의 많은 색인 기법들이 연구되었다. 하지만 기존 기법들은 k-최근접 객체들을 연산하기 위해 불필요한 셀을 접근하여 연산 자원을 낭비하거나 근접한 셀을 알아내는데 매우 큰 연산 비용을 초래한다. 그래서 본 논문에서는 한 셀과 주변 셀과의 거리 관계를 나타내는 거리 관계 패턴을 이용하여 k-최근접 질의 처리시 적은 연산 비용과 적은 저장 공간을 사용하는 새로운 k-최근접 질의 처리 기법을 제안한다. 본 논문에서는 k-최근접 질의 처리의 대표적인 기법인 CPM과 성능을 비교하여 제안하는 기법의 우수성을 입증한다.

Keywords

References

  1. X. Yu, K. Pu, and N. Koudas, 'Monitoring knearest Neighbor Queries over Moving Objects,' Proc. International Conference on Data Engineering, pp. 631-642, 2005 https://doi.org/10.1109/ICDE.2005.92
  2. K. Mouratidis, M. Hadjieleftheriou, and D. Papadias, 'Conceptual Partitioning: An Efficient Method for Continuous Nearest Neighbor Monitoring,' Proceedings of the ACM Conference on Management of Data, pp. 634-645, 2005 https://doi.org/10.1145/1066157.1066230
  3. S. Prabhakar, Y. Xia, D. V. Kalashnikov, W. G. Aref and S. E. Hambrusch, 'Query Indexing and Velocity Constrained Indexing : Scalable Techniques for Continuous Queries on Moving Objects,' IEEE Transactions on Computers, Vol.51, No.10, pp. 1124-1140, 2002 https://doi.org/10.1109/TC.2002.1039840
  4. M. F. Mokbel, X. Xiong, and W. G. Aref, 'SINA: Scalable Incremental Processing of Continuous Queries in Spatio-temporal Databases,' Proc. ACM SIGMOD International Conference on Management of Data, pp. 623-634, 2004 https://doi.org/10.1145/1007568.1007638
  5. X. Wang, Q. Zhang, W. Sun, W. Wang, and B. Shi, 'cGridex: Efficient Processing of Continuous Range Queries over Moving Objects,' Proc. International Conference on Advances in Web-Age Information Management, pp. 345-356, 2005 https://doi.org/10.1007/11563952_31
  6. D. V. Kalashnikov, S. Prabhakar, and S. E. Hambrusch, 'Main Memory Evaluation of Monitoring Queries over Moving Objects,' Distributed and Parallel Databases, Vol. 15, No.2, pp. 117-135, 2004 https://doi.org/10.1023/B:DAPD.0000013068.25976.88
  7. K. L. Wu, S. K. Chen, and P. S. Yu, 'Processing continual range queries over moving objects using VCR-based query indexes,' Proc. IEEE International Conference on Mobile and Ubiquitous Systems : Networking and Services, pp. 226-235, 2004 https://doi.org/10.1109/MOBIQ.2004.1331729
  8. K. L. Wu, S. K. Chen, and P. S. Yu, 'On Incremental Processing of Continual Range Queries for Location -Aware Services and Applications,' Proc. Annual International Conference on Mobile and Ubiquitous Systems, pp. 261-269, 2005 https://doi.org/10.1109/MOBIQUITOUS.2005.42
  9. K. Mouratidis and D. Papadias, 'Continuous Nearest Neighbor Monitoring in Road networks,' Proc. the Very Large data bases Conjerence (VLDB), 2006
  10. K. Mouratidis and D. Papadias, 'Continuous Nearest Neighbor Queries over Sliding Windows,' IEEE Transactions on Knowledge and data Engineering(TKDE), Vo.19, No.6, pp. 789-803, 2007 https://doi.org/10.1109/TKDE.2007.190617