Journal of KIISE:Databases (한국정보과학회논문지:데이타베이스)

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

Efficient Accesses of R-Trees for Distance Join Query Processing in Multi-Dimensional Space

다차원 공간에서 거리조인 질의처리를 위한 R-트리의 효율적 접근

  • 신효섭 (서울대학교 컴퓨터공학부) ;
  • 문봉기 (미국 아리조나대학교 전산학과) ;
  • 이석호 (서울대학교 컴퓨터공학부)
  • Published : 2002.02.01
Download PDF
⟨ Previous Next ⟩

Abstract

The distance join is a spatial join which finds data pairs in the order of distance between two spatial data sets using R-trees. The distance join stores node pairs in a priority queue, which are retrieved while traversing R-trees in a top-town manner, in the order of distance. This paper first shows that a priority strategy for the tied pairs in the priority queue during distance join processing has much effect on its performance, and then proposes an optimized secondary priority method. The experiments show that the proposed method is always better than the other methods in the performance perspectives.

거리조인은 R-트리를 사용하여 두 공간 데이터 집합 사이의 데이터쌍을 거리 상 가까운 순으로 검색하는 공간조인이다. 거리조인은 R-트리를 하향식으로 순회하면서 생성되는 노드쌍들을 거리값 순으로 우선순위 큐에 저장한다. 본 논문에서는 거리조인 처리시 우선순위 큐 안에서 동점자 노드쌍들의 우선 순위 정책이 알고리즘의 성능을 많이 좌우할 수 있음을 보여주고, 이를 위한 최적화된 2차 우선 순위 기법을 제안한다. 실험을 통하여, 제안한 기법이 다른 기법에 비하여 항상 좋은 성능을 나타냄을 보여준다.

Keywords

References

  1. Hyoscop Shin, Bongki Moon, and Sukho Lee, 'Adaptive Multi-Stagc Distance Join Processing,' Proc. of 2000 ACM-SIGMOD Conference, pages 343-354, Dallas, TX, May 2000
  2. 신효섭, 이석호, '공간 데이타베이스에서 최근접 K쌍을 찾는 효율적 기법', 정보과학회 논문지: 데이타베이스, 27권 2호, pp.238-246, 2000. 6.
  3. Gisli R. Hjaltason, and Hanan Samet, 'Incremental Distance Join Algorithms for Spatial Databases,' Proc. of 1998 ACM-SIGMOD Conference, pages 237- 248, Seattle, WA, June 1998 https://doi.org/10.1145/276304.276326
  4. Antonio Corral, Yannis Manolopoulos, Yannis Thcodoridis, and Michael Vassilakopoulos, 'Closest Pair Quenes in Spatial Databases,' Proc. of 2000 ACM -SIGMOD Conference, pages 189 -200, Dallas, TX, May 2000
  5. Antonin Guttman, 'R-Trees: A Dynamic Index Structure for Spatial Searching,' Proc. of .1984 ACM-SIGMOD conference, pages 47-57, Boston, MA, June 1984 https://doi.org/10.1145/602259.602266
  6. Norbert Beckmann, Hans-Peter Kriegel, Ralf Schneider, and Bernhard Seeger, 'The R*-trec: An Efficient and Robust Access Method for Points and Rectangles,' Proc, of 1990 ACM-SIGMOD conference, pages 322-331, Atlantic City, NJ, May 1990
  7. Timos K. Sellis, Nick Roussopoulos, and Christos Faloutsos, 'The R+-Tree: A Dynamic Index for Multi-Dimensional Objects,' Proc. of 1987 VLDB conference, pages 507-518, Brighton. England, September 1987
  8. Stefan Berchtold, Daniel A. Keim, and Hans-Peter Kriegel, 'The X-Tree: An Index Structure for High-Dimensional Data,' Proc. of 1996 VLDB conference, pages 28-39, Mumbai(Bombay), India, September 1996
  9. Andreas Henrich, 'The $LSD^h-tree:$ An Access Structure for Feature Vectors,' Proc. of 1998 ICDE conference, pages 362-369, Orlando, FL, February 1998
  10. John T. Robinson, 'The K-D-B-tree: A Search Structure for Large Multidimensional Dynamic Indexes,' Proc. of 1981 ACM-SIGMOD conference, pages 10-18, Ann Arbor, Michigan, April 1981 https://doi.org/10.1145/582318.582321