Browse > Article

A Refinement Strategy for Spatial Selection Queries with Arbitrary-Shaped Query Window  

유준범 (한국과학기술원 전산학과)
최용진 (한국과학기술원 전산학과)
정진완 (한국과학기술원 전산학과)
Publication Information
Journal of KIISE:Databases / v.30, no.3, 2003 , pp. 286-295 More about this Journal
Abstract
The shape of query windows for spatial selection queries is a rectangle in many cases. However, it can be issued for spatial selection queries with not only rectangular query widow, but also polygonal query window. Moreover, as the applications like GIS can manage much more spatial data, they can support the more various applications. Therefore it is valuable for considering about the query processing method suitable for not only rectangle query window, but also general polygonal one. It is the general state-of-the-art approach to use the plane- sweep technique as the computation algorithm in the refinement step as the spatial join queries do. However, from the observation on the characteristics of spatial data and query windows, we can find in many cases that the shape of query window is much simpler than that of spatial data. From these observations, we suggest a new refinement process approach which is suitable for this situation. Our experiments show that, if the number of vertices composing the query window is less than about 20, the new approach we suggest is superior to the state-of-the-art approach by about 20% in general cases.
Keywords
Spatial Database; Spatial Selection Query; Refinement Strategy;
Citations & Related Records
연도 인용수 순위
  • Reference
1 N. Beckmann, H.-P. Kriegel, R. Schnieder, and B. Seeger, 'The Rv-tree: an efficient and robust access method for points and rectangles,' ACM SIGMOD conference pp322-331, 1990   DOI
2 I. Kamel and C. Faloutsos, 'Hilbert R-tree: An Improved Rrtree Using Fractals,' VLDB pp500-509, 1994
3 G. Evangelidis, D. Lomet, and B. Salzberg, 'The hb-tree: A modified hB-tree supporting concurrency, recovery, and node consolidation,' VLDB pp551 -561, 1995
4 J. A. Orenstein, 'A Comparison of Spatial Query Processing Techniques for Native and Parameter Space,' ACM SIGMOD conference pp343-352, 1990   DOI
5 A. Aboulnaga and J. F. Naughton, 'Accurate Estimation of the Cost of Spatial Selections,' IEEE ICDE pp123-134, 2000   DOI
6 T. Brinkhoff, H. Kriegel, and B. Seeger, 'Efficient Processing of Spatial Joins Using R'-trces,' ACM SIGMOD conference pp237-246,1993   DOI
7 M L. Lo and C. V. Ravishankar, 'Spatial Hash-Joins,' ACM SIGMOD conference pp247 258, 1996   DOI
8 Y. W. Huang, M. Jones, and E. A. Rundensteiner, 'Improving Spatial Intersect Join Using Symbolic Intersect Detection,' Symposium on Large Spatial Databases(SSD) pp165-177, 1997
9 T. Brinkhoff, H.P. Kriegel, R. R. Schnieder, and B. Seeger, 'Multi-Step Processing of Spatial Joins,' ACM SIGMOD conference pp197 208, 1994   DOI
10 J. M. Patel and D. J. DeWitt, 'Partition Based Spatial-Merge Joins,' ACM SIGMOD conference pp259-270, 1996   DOI
11 M. I. Shamos and D. J. Hoey, 'Geometric Insertion Problems,' IEEE Symposium on Foundations of Computer Sciences(FOCS) pp208-215, 1976
12 Bureau of the Census, 'TIGER/Line Files, 1995: Technical Documentation,' 1996