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Caching Strategies for Spatial Joins

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Abstract

The filter-and-refine strategy is well-established as the basis for spatial join algorithms. In contrast to the filter step, the refinement step has received little attention, despite contributing significantly to the total cost of a join evaluation. This paper reports investigations of spatial join algorithms for z-ordering and R-trees, with particular emphasis on interactions between choices of algorithms for the filter, sequencing and refinement steps and on the effects of clustered and unclustered organization of full spatial descriptions of objects. Our experiments show that while it is in general desirable to introduce an additional housekeeping step to reduce I/O costs of the refinement step, it is not necessary in all cases. In addition, we propose a new caching strategy for spatial joins, called zig-zag, which outperforms its competitors in all but one case. These results suggest that spatial joins need caching strategies other than non-spatial ones. Furthermore, our experiments confirm that the choice of the sequencing strategy used is very important and that clustering has a significant influence on join performance.

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References

  1. D.J. Abel. “Some evolutionary paths for spatial databases,” in Int. Symp. on Next Generation Databases and their Applications NDA'93, Fukuoka, 1–10, 1993.

  2. D.J. Abel. “SIRO-DBMS: A database toolkit for geographical information systems,” Int. J. Geographical Information Systems, Vol. 4(3):443–464, 1989.

    Google Scholar 

  3. W.G. Aref and H. Samet. “The spatial filter revisited,” in Proc. 6th Int. Symp. on Spatial Data Handling (SDH'94), 190–208, 1994.

  4. L. Arge, O. Procopiuc, S. Ramaswamy, T. Suel, and J.S. Vitter. “Scalable sweeping-based spatial join,” in Proc. 24th Int. Conf. on Very Large Data Bases, 570–581, 1998.

  5. N. Beckmann, H.-P. Kriegel, R. Schneider, and B. Seeger. “The R*-tree: an efficient and robust access method for points and rectangles,” in Proc. ACM SIGMOD Int. Conf. on Management of Data, 322–331, 1990.

  6. T. Brinkhoff, H. Horn, H.-P. Kriegel, and R. Schneider. “A storage and access architecture for efficient query processing in spatial database systems,” in D. Abel and B. C. Ooi (Eds.), Proc. 3th Int. Symp. on Spatial Databases (SSD'93), Number 692 in LNCS, Berlin/Heidelberg/New York, 357–376, Springer-Verlag, 1993.

    Google Scholar 

  7. T. Brinkhoff and H.-P. Kriegel. “The impact of global clustering on spatial database systems,” in Proc. 20th Int. Conf. on Very Large Data Bases, 168–179, 1994.

  8. T. Brinkhoff, H.-P. Kriegel, and B. Seeger. “Efficient processing of spatial joins using R-trees,” in Proc. ACM SIGMOD Int. Conf. on Management of Data, 237–246, 1993.

  9. V. Gaede. “Geometric information makes spatial query processing more efficient,” in Proc. 3rd ACM Int. Workshop on Advances in Geographic Information Systems (ACM-GIS'95), Baltimore, Maryland, USA, 45–52, 1995.

  10. V. Gaede. “Optimal redundancy in spatial database systems,” in M. J. Egenhofer and John R. Herring (Eds.), Proc. 4th Int. Symp. on Spatial Databases (SSD'95), Vol. 951 of LNCS, Berlin/Heidelberg/New York, 96–116, Springer-Verlag, 1995.

    Google Scholar 

  11. V. Gaede and O. Günther. “Survey on multidimensional access methods,” ACM Computing Survey, Vol. 30(2), 1998.

  12. V. Gaede and W.-F. Riekert. “Spatial access methods and query processing in the object-oriented GIS GODOT,” in Proc. of the AGDM'94 Workshop, Delft, The Netherlands, Netherlands Geodetic Commission, 40–52, 1994.

  13. O. Günther. “Efficient computation of spatial joins,” in Proc. 9th IEEE Int. Conf. on Data Eng., 50–59, 1993.

  14. R.H. Güting and W. Shilling. “A practical divide and conquer algorithm for the rectangle intersection problem,” Information Science, Vol. 42:95–112, 1987.

    Google Scholar 

  15. A. Guttman. “R-trees: A dynamic index structure for spatial searching,” in Proc. ACM SIGMOD Int. Conf. on Management of Data, 47–54, 1984.

  16. Y.-W. Huang, N. Jing, and E.A. Rudensteiner. “Spatial joins using R-trees,” in Proc. 23th Int. Conf. on Very Large Data Bases, 396–405, 1997.

  17. H.V. Jagadish. “Linear Clustering of Objects with Multiple Attributes,” in Proc. ACM SIGMOD Int. Conf. on Management of Data, 332–342, 1990.

  18. M.L. Lo and C.V. Ravishankar. “Generating seeded trees from data sets,” in M. J. Egenhofer and J. R. Herring (Eds.), Proc. 4th Int. Symp. on Spatial Databases (SSD'95), Vol. 951 of LNCS, Berlin/Heidelberg/New York, 328–347, Springer-Verlag, 1995.

    Google Scholar 

  19. M.L. Lo and C.V. Ravishankar. “Spatial hash-join,” in Proc. ACM SIGMOD Int. Conf. on Management of Data, 247–258, 1996.

  20. M.L. Lo and C.V. Ravishankar. “The design and implementation of seeded trees: an efficient method for spatial joins,” IEEE Trans. Knowledge and Data Eng., Vol. 10(1):136–152, 1998.

    Google Scholar 

  21. J. Orenstein. “Strategies for optimizing the use of redundancy in spatial databases,” in A. Buchmann and O. Günther, T. R. Smith, and Y.-F. Wang (Eds.), Proc 1st Int. Symp. on Spatial Databases (SSD'89), Vol. 409 of LNCS, Berlin/Heidelberg/New York, 115–134, Springer-Verlag, 1989.

    Google Scholar 

  22. J. Orenstein and F.A. Manola. “Probe spatial data modeling and query processing in an image database application,” IEEE Trans. Software Eng., Vol. 14(5):611–629, 1988.

    Google Scholar 

  23. J.M. Patel and D.J. DeWitt. “Partition based spatial-merge join,” in Proc. ACM SIGMOD Int. Conf. on Management of Data, Canada, 259–270, 1996.

  24. M. Stonebraker, J. Frew, and J. Dozier. “The SEQUOIA 2000 Project,” in D. Abel and B. C. Ooi (Eds.), Proc. 3th Int. Symp. on Spatial Databases (SSD'93), Vol. 692 of LNCS, Berlin/Heidelberg/New York, 397–412, Springer-Verlag, 1993.

    Google Scholar 

  25. P. Valduriez. “Join indices,” ACM Trans. Database Systems, Vol. 12(2):219–246, 1987.

    Google Scholar 

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Abel, D.J., Gaede, V., Power, R.A. et al. Caching Strategies for Spatial Joins. GeoInformatica 3, 33–59 (1999). https://doi.org/10.1023/A:1009844729517

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