DOI QR코드

DOI QR Code

Distance Browsing Query Processing using Query Result Set

질의 결과를 이용한 거리 브라우징 질의의 처리

  • 박동주 (숭실대학교 컴퓨터학부) ;
  • 박상원 (한국외국어대학교 컴퓨터및정보통신공학부) ;
  • 정태선 (아주대학교 정보및컴퓨터공학부) ;
  • 이상원 (성균관대학교 정보통신공학부)
  • Published : 2005.10.01

Abstract

Distance browsing queries, namely k-nearest neighbor queries, are the most important queries in spatial database applications, e.g., Geographic Information Systems(GISs). Recently, GIS applications trends to extend themselves toward wide multi-user environments such as the Web. Since many techniques for such queries, where Hjaltason and Samet's algorithm is the most efficient one, were optimized for only one query, we need to complement them suitable for multi-user environments. It can be a good approach that we store many individual query results in a cache, i.e., query result caching and reuse them in evaluating incoming queries, j.e., query result matching. In this paper, we propose a complementary Hjaltason and Samet's algerian capable of reusing previous query results in a cache for answering distance browsing queries in multi-user GIS environments. Our experimental results conform the efficiency of our approach.

k-최근접 질의와 같은 거리 브라우징 질의는 지리정보시스템(GIS)과 같은 공간 데이터베이스 응용에서 아주 중요한 질의이다. 최근 GIS 응용은 웹과 같은 다중 사용자 환경으로 확장되고 있는 추세이다. 이러한 질의를 처리하기 위한 많은 기법들 중에서 Hjaltason과 Samet이 제안한 알고리즘이 가장 우수하지만, 하나의 질의 처리에 대해서만 최적화가 이루어졌다. 따라서 다중 사용자 환경에 적합하도록 이러한 기법들을 보완할 필요성이 있다. 이전에 처리된 질의 결과를 캐쉬에 저장해 두고(즉, 질의 결과 캐슁 기법) 후속 질의를 처리할 때 질의 결과를 이용하는 (즉, 질의 결과 매칭 기법) 것은 하나의 좋은 접근 방법이라 할 수 있다. 본 논문은 다중 사용자 GIS 환경에서 거리 브라우징 질의를 효율적으로 처리하기 위해서 캐쉬된 이전 질의 결과를 재사용할 수 있도록 보완된 Hjaltason & Samet의 알고리즘을 제안한다. 실험 결과를 통해 우리의 접근 방법이 효율적임을 보인다.

Keywords

References

  1. GeoWeb Project http://wings.buffalo.edu/geoweb
  2. The ArcExplorer GIS data viewer. http//www.esri.com/software/ arcexplorer/index.html
  3. T. Barchlay, D. Slutz, and J. Gray. TerraServer: A Spatial Data Warehouse. In Proceedings of ACM SIGMOD 2000 IntI. Conf. on Management of Data, May, 2000
  4. N. Beckmann, H. P. Kriegel, R. Schneider, and B. Seeger. The R*-tree: An Efficient And Robust Access Method for Points and Rectangles. In Proceedings of the ACM SIGMOD Conference, June, 1990
  5. B. Braunmuller, M. Ester, H.-P. Kriegel, and ]. Sander. Efficiently Supporting Multiple Similarity Queries for Mining in Metric Databases. In Proceedings of the 2000. IEEE International Conference on Data Engineering, February, 2000 https://doi.org/10.1109/ICDE.2000.839418
  6. A. J Broder. Strategies for Efficient Incremental Nearest Neighbor Search. Pattern Recognition, 23(1-2), January, 1990 https://doi.org/10.1016/0031-3203(90)90057-R
  7. K Chakrabarti, K. Porkaew, and S. Mehrotra. Efficient Query Refinement in Multimedia Databases. In Proceedings of the 2000 IEEE International Conference on Data Engineering, February, 2000
  8. C. M. Chen and R. Roussopoulos. The Implementation and Performance Evaluation of the ADMS Query Optimizer: Integrating Query Result Caching and Matching. In 4th International Conference on Extending Database Technology, March, 1994
  9. Serena Coetzee and Judith Bishop, 'New way to query GISs on the Web,' IEEE Software, Volume 15, Issue 3, pp. 31-40, May-June 1998 https://doi.org/10.1109/52.676719
  10. D Comer. The Ubiquitous B-tree. ACM Computing Surveys, 11(2), June, 1979 https://doi.org/10.1145/356770.356776
  11. P. M. Deshpande, K Ramasamy, and A. Shukla. Caching Multidimensional Queries Using Chunks. In Proceedings of ACMl SIGMOD Conference, June, 1997 https://doi.org/10.1145/276304.276328
  12. S. Finkelstein. Common Expression Analysis in Database Applications. In Proceedings of ACM SIGMOD Conference, June, 1982
  13. A. Guttman, 'R-Trees: A Dynamic Index Structure for Spatial Searching,' In Proceedings of ACM SIGMOD International Conference on Management of Data, pp.47-57, Jun., 1984 https://doi.org/10.1145/602259.602266
  14. V. Harinarayan, A. Rajaraman, and J.D. Ullman, 'Implementing Data Cubes Efficiently.,' ACM SIGMOD 1996, pp 205-216, 1996 https://doi.org/10.1145/233269.233333
  15. A. Henrich. A Distance-scan Algorithm for Spatial Access Structures. In Proceedings of the Second ACM Workshop on Geographic Information Systems, December, 1994
  16. G. R. Hjaltason and H. Samet. Distance Drowsing in Spatial Databases. ACM Transactions on Database Systems, 24(2), June, 1999 https://doi.org/10.1145/320248.320255
  17. Y. Kotidis and N. Roussopoulos. DynaMat: A Dynamic View Management System for Data Warehouses. In Proceedings of ACM SIGMOD Conference, June, 1998 https://doi.org/10.1145/304182.304215
  18. L.-A. Larson and H. Z. Yang. Computing Queries From Derived Relations. In Proceedings of VLDB Conference, August, 1985
  19. D.-J. Park and H.-J. Kim. An Enhanced Technique for k-Nearest Neighbor Queries with Non-spatial Selection Predicates. Multimedia Tools and Applications, 19(1), January, 2003 https://doi.org/10.1023/A:1021121030238
  20. N. Roussopoulos, S. Kelley, and F. Vincent. Nearest Neighbor Queries. In Proceedings of the ACM SIGMOD Conference, May, 1995 https://doi.org/10.1145/223784.223794
  21. Michael Stonebraker, James Frew, Kenn Gardels, and Jeff Meredith. The Sequoia 2000 Benchmark. In Proceedings of ACM SIGMOD Conference, June, 1993 https://doi.org/10.1145/170035.170038