Browse > Article

An Efficient Spatial Join Method Using DOT Index  

Back, Hyun (한림대학교 컴퓨터공학과)
Yoon, Jee-Hee (한림대학교 정보통신공학부)
Won, Jung-Im (한양대학교 정보통신학부)
Park, Sang-Hyun (연세대학교 컴퓨터과학과)
Publication Information
Journal of KIISE:Databases / v.34, no.5, 2007 , pp. 420-436 More about this Journal
Abstract
The choice of an effective indexing method is crucial to guarantee the performance of the spatial join operator which is heavily used in geographical information systems. The $R^*$-tree based method is renowned as one of the most representative indexing methods. In this paper, we propose an efficient spatial join technique based on the DOT(Double Transformation) index, and compare it with the spatial Join technique based on the $R^*$-tree index. The DOT index transforms the MBR of an spatial object into a single numeric value using a space filling curve, and builds the $B^+$-tree from a set of numeric values transformed as such. The DOT index is possible to be employed as a primary index for spatial objects. The proposed spatial join technique exploits the regularities in the moving patterns of space filling curves to divide a query region into a set of maximal sub-regions within which space filling curves traverse without interruption. Such division reduces the number of spatial transformations required to perform the spatial join and thus improves the performance of join processing. The experiments with the data sets of various distributions and sizes revealed that the proposed join technique is up to three times faster than the spatial join method based on the $R^*$-tree index.
Keywords
spatial join; spatial indexing method; space filling curve; DOT index;
Citations & Related Records
연도 인용수 순위
  • Reference
1 C. Bohm, S. Beerchtold, and D. Keim, 'Searching in High Dimensional Spaces - Index Structures for Improving the Performance of Multimedia Databases,' ACM Computing Surveys, Vol. 33, No. 3, pp. 322-373, 2001   DOI   ScienceOn
2 C. Faloutsos, and S. Roseman, 'Fractals for Secondary Key Retrieval,' Proc. PODS, pp. 247-252, 1989
3 최익수, 윤지희, 이건배, 'DOT 공간색인 기법을 이용한 공간조인 알고리즘', 한국정보과학회 데이타베이스 연구회, 동계 데이타베이스 학술대회논문집, 제14권 1 호, pp. 21-26, 1998
4 T. Sellis, N. Roussopoulos and C. Faloutsos, 'The $R^+-tree:$ A Dynamic Index for Multidimensional Objects,' Proc. 13th VLDB Conf., pp. 507-518, 1987
5 O. Gunther, The Cell Tree: An Index for Geometric Data, Memorandum No. UCB/ERL M86/89, Univ. of California, Berkeley, December 1986
6 Y. Tao and D. Papadias, 'MV3R-Tree: A Spatio-Temporal Access Method for Timestamp and Interval Queries', Proc. VLDB Conf., pp. 431-440, 2001
7 D. Papadias, J. Zhang, N. Mamoulis, and Y. Tao, 'Query Processing in Spatial Network Databases,' Proc. VLDB Conf., pp. 802-813, 2003
8 http://www.rtreeportal.org (accessed 10 May 2007)
9 N. Beckmann, H. Kriegel, and R. Schneider, 'The R*-tree : An Efficient and Robust Access Method for Points and Rectangles,' Proc. ACM SIGMOD, pp. 322-331, 1990
10 Y. Theodoridis, M. Vazirgiannis, and T. K. Sellis, 'Spatio-Temporal Indexing for Large Multimedia Applications,' Proc. IEEE Intl. Conf. on Multimedia Computing and Systems, pp. 441-448, 1996
11 D. Pfoser, Y. Theodoridis, and C. S. Jensen, 'Indexing Trajectories in Query Processing for Moving Objects,' Chorochronos Technical Report, CH-99-3, 1999
12 J. Song, K. Whang, Y. Lee, M. Lee, and S. Kim, 'Spatial Join Processing Using Corner Transformation,' IEEE Trans. Knowledge and Data Eng., vol. 11, no. 4, pp. 688-695, 1999   DOI   ScienceOn
13 J. T. Robinson, 'The K-D-B-tree: A Search Structure for Large Multidimensional Dynamic Indexes,' ACM SIGMOD Conf., pp. 10-18, 1981
14 J. Orenstein, 'Spatial Query Processing in an Object-Oriented Database System,' Proc. ACM SIGMOD, pp. 326-336, 1986
15 J. Orenstein, F. Manola, 'PROBE Spatial Data Modeling and Query Processing in an Image Database Applications,' IEEE Trans. on Software Engineering, Vol 14, No. 5, pp. 611-629, 1988   DOI   ScienceOn
16 H. W. Six and P. Widmayer, 'Spatial Searching in geometric Databases,' Proc. 4th Intl. Conf. on Data Engineering, pp. 496-503, 1988
17 J. Lawder and P. King, 'Querying Multidimensional Data Indexed Using the Hilbert Space-Filling Curve,' ACM SIGMOD Record, Vol. 30, No. 1, pp. 19-24, 2001   DOI   ScienceOn
18 H. Kriegel, M. Schiwietz, R. Schneider, and B. Seeger, 'Performance Comparison of Point and Spatial Access Methods,' SSD89, Lecture Note, pp. 89-114, 1989
19 A. Guttman, 'R-trees: A Dynamic Index Structures for Spatial Searching,' Proc. ACM SIGMOD, pp. 47-57, 1984
20 T. Brinkhoff, H. P. Kriegel, and B. Seeger, 'Efficient Processing of Spatial Joins Using Rtrees,' Proc. ACM SIGMOD, pp. 237-246, 1993   DOI   ScienceOn
21 A. Henrich, H.-W. Six, and P. Widmayer, 'The LSD Tree: Spatial Access to Multidimensional Point and Non Point Objects,' Proc. 15th VLDB Conf., pp 45-53, 1989
22 D. Pfoser, C. S. Jensen, and Y. Theodoridis, 'Novel Approaches in Query Processing for Moving Objects', Proc. VLDB Conf., pp. 395-406, 2000
23 C. Faloutsos, and Y. Rong, 'DOT : A Spatial Access Method Using Fractals,' Proc. 7th Intl. Conf. on Data Engineering, pp. 152-159, 1991
24 H. Dai and H. Su, Approximation and Analytical Studies of Inter-cluster Performances of Space-Filling Curves, Discrete Mathematics and Theoretical Computer Science, pp. 53-68, 2003
25 S. Gupta, S. Kopparty, and C. Ravishankar, 'Roads, Codes, and Spatiotemporal Queries,' Proc. ACM PODS, pp. 115-124, 2004
26 K. Hinrichs, J. Nievergelt, 'The Grid File : A Data Structure Designed to Support Proximity Queries on Spatial Objects,' Proc. Workshop on Graph Theoretic Concepts in Computer Science, pp. 100-113, 1983
27 B. Moon, H. V. Jagadish, C. Faloutsos, and J. Saltz, 'Analysis of the Clustering Properties of Hilbert Space-Filling Curve,' IEEE Trans. on Knowledge and Data Eng., Vol. 13, No. 1, pp. 124-141, 2001   DOI   ScienceOn