Journal of KIISE:Databases (한국정보과학회논문지:데이타베이스)

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

Cost Model for Parallel Spatial Joins using Fixed Grids

고정 그리드를 이용한 병렬 공간 조인을 위한 비용 모델

  • Kim, Jin-Deog (Dept.of Computer Engineering, Dongeui University) ;
  • Hong, Bong-Hee (Dept.of Computer Science Engineering, Busan National University)
  • Published : 2001.12.01
Download PDF
⟨ Previous Next ⟩

Abstract

The most expensive spatial operation in patial database in a spatial join which computes a combined table of which tuple consists of two tuples of the two tables satisgying a spatial predicate. Although the execution time of sequential processing of a spatial join has been so far considerably improved the response time is not tolerable because of not meeting the requiremetns of interactive users. It is usually appropriate to use parallel processing to improve the performance of spatial join processing. in spatial database the fixed grids which consist of the regularly partitioned cells can be employed the previous works on the spatial joins have not studied the parallel processing of spatial joins using fixed grids. This paper has presented an analytical cost model that estimates the comparative performance of a parallel spatial join algorithm based on the fixed grids in terms of the number of MBR comparisons. disk accesses, and message passing, Several experiments on the synthetic and real datasets show that the proposed analytical model is very accurate. This most model is also expected to used for implementing a very important DBMS component, Called the query processing optimizer.

공간 데이타베이스에서 가장 비용이 큰 공간 연산자는 공간 조인이다. 공간 조인은 두개의 데이타 집합으로부터 공간적인 조건을 만족하는 두 객체 쌍의 집함을 구하는 것이다. 지난 수년동안 공간 조인의 순차 수행 시간은 많이 향상되었지만, 그 웅답시간은 사용자의 요구를 만족시키지 못하고 있다. 그래서 공간조 인의 병렬 수행에 대한 연구가 자연스럽게 대두되고 있다. 공간 데이타베이스 관리 시스템에서 공간 데이타 의 관리의 용이성 및 부분 지역 검색의 효율성 등을 위해 고정 크기의 격자 구조를 갖는 고정 그리드를 이용 할 수 있다. 그러나 지금가지 고정 그리드를 이용한 공간조인의 병렬 처리에 관한 연구는 거의 없다. 이 논문에서는 고정 그리드를 이용한 병렬 공간 조인 알고리즘의 성능을 예측하는 비용 모델을 제시하 였는데, 이는 최소 경계 사각형(Minimum Bounding Rectangle : MBR)의 비교 횟수. 디스크 접근 횟수,메시지 전송 횟수 등을 근거로 하였다. 실제 데이타 및 인위 데이타 집합을 이용한 실험은 제안한 비용 모델이 정확함을 보여주었다. 이 비용 모델은 복합 공간 질의의 비용을 예측할 필요가 있는 공간 질의 최 적화를 위한 유용한 도구가 될 것으로 기대된다.

Keywords

References

  1. O. Gnther, Efficient Computation of Spatial Joins, Proc, of Int. Conf. on Data Engineering, pp. 50-59, 1993 https://doi.org/10.1109/ICDE.1993.344078
  2. T. Brinkhoff, H.P. Kriegel, RSchneider, B. Seeger, Efficient Processing of Spatial joins Using Rtrees, Proc. of Int. Conf. on Management of Data, ACM SIGMOD, pp. 237-246, 1993 https://doi.org/10.1145/170035.170075
  3. T. Brinkhoff, H.P. Kriegel, R.Schneider, B. Seeger, Multi-Step Processing of Spatial Joins, Proc. of Int. Conf. on Management of Data, ACM SIGMOD, pp. 197-208, 1994 https://doi.org/10.1145/191839.191880
  4. R Laurini, D. Thompson, Fundamentals of Spatial Information Systems, Academic Press, 1992
  5. D. Rotem, Spatial Join Indices, Proc. of Int. Conf. on Data Engineering, pp. 500-509, 1991
  6. J. A. Orestein, Redundancy in spatial databases, Proc. of Int. Conf. on Management of Data, ACM SIGMOD, pp. 294-305, 1989 https://doi.org/10.1145/67544.66954
  7. M.L. La, C.V. Ravishankar, Spatial Hash-Joins, Proc. of Int. Conf. on Management of Data, ACM SIGMOD, pp. 247-258, 1996
  8. J.M. Patel, D,J. Dewitt, Partition based spatial merge Join, Proc. of Int. Conf. on Management of Data, ACM SIGMOD, pp. 259-270, 1996 https://doi.org/10.1145/233269.233338
  9. J.W. Song, K.Y. Whang, Y.K.Lee, M.J.Lee, SW. Kim, Spatial Join Processing Using Corner Trans 688-695, 1999
  10. T. Brinkhoff, H.P. Kriegel, B. Seeger, Parallel Processing of Spatial Joins Using R- trees, Proc. of Int. Conf. on Data Engineering, pp. 258-265, 1996 https://doi.org/10.1109/ICDE.1996.492114
  11. Y.W. Huang, N. Jing, E. A. Rundensteiner, Spatial joins using R-tree : Breadth-first traversal with global optimizations, Proc. of Int. Conf. on VLDB, pp. 396-405, 1997
  12. E.G. Hoel, H. Samet, Data-Parallel Spatial Join Algorithms, Proc. of Int. Conf. on Parallel Processing, pp. 227-234, 1994 https://doi.org/10.1109/ICPP.1994.82
  13. X. Zhou, D. J. Abel, David Truffet, Data Partitioning for Parallel Spatial join Processing, Proc. of Int. Conf. on SSD, PP. 178-196, 1997 https://doi.org/10.1007/3-540-63238-7_30
  14. M.L. Lo, C.V. Ravishankar, Spatial joins Using Seeded Trees, Proc. of Int. Conf. on Management of Data, ACM SIGMOD, pp. 209-220, 1994 https://doi.org/10.1145/191839.191881
  15. N. Koudas, K.C. Sevcik, Size Separation Spatial join, Proc. of Int. Conf. on Management of Data, ACM SIGMOD, pp, 324-335, 1997 https://doi.org/10.1145/253260.253340
  16. W.G. Aref, H. Samet, Cascaded Spatial join Algorithms with Spatially Sorted Output, Proc. of Int. Conf. on ACM GIS, pp. 17-24, 1997 https://doi.org/10.1145/258319.258327
  17. L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, J.S. Vitter, Scalable Sweeping Based Spatial Join, Proc. of Int. Conf. on VLDB, pp, 570-581, 1998
  18. N. Mamoulis, D. Papadias, Integration cif Spatial join Algorithms for Processing Multiple Inputs, Proc. of Int. Conf. on Management of Data, ACM SIGMOD, pp. 1-12, 1999 https://doi.org/10.1145/304182.304183
  19. Y.W. Huang, N. Jing, E. A. Rundensteiner, A Cost Model for Estimating the Performance of Spatial Joins Using R-trees, Proc. of Int. Conf. on SSDBM, pp, 30-38, 1997 https://doi.org/10.1109/SSDM.1997.621148
  20. D. Papadias, N. Mamoulis, Y. Theodoridis, Processing and Optimization of Multiway Spatial Joins Using R-trees. Proc, of Int. Conf. on PODS, pp. 44-55, 1999 https://doi.org/10.1145/303976.303981
  21. Y. Theodoridis, T. Sellis, A Model for the Prediction of $R^{\ast}$-tree Performance, Proc. of Int. Symp, on ACM PODS, pp. 161-171, 1996 https://doi.org/10.1145/237661.237705
  22. Y. Theodoridis, E. Stefanakis, T. Sellis, Cost Models for Join Queries in Spatial Databases, Proc. of Int. Conf. on Data Engineering, pp.476-483, 1998 https://doi.org/10.1109/ICDE.1998.655810
  23. http://epoch.cs.berkelev.edu:8000/sequoia/benchmark/polvgon/, Sequoia 2000 FTP server home page
  24. http://www.enst.fr/~bdtest/sigbench/index.html., Spatial Join Benchmarking home page
  25. D.J. DeWitt, DIRECT-A Multiprocessor Organization for Supporting Relational Database Management System, IEEE Trans. on Computers, pp, 395-406, 1979 https://doi.org/10.1109/TC.1979.1675379
  26. J.D. Kim, B.H. Hong, Parallel Spatial join Algorithms using Grid Files, Proc. of Int. Symp. on DANTE'99, pp. 127-135, 1999 https://doi.org/10.1109/DANTE.1999.844964