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Incremental distance join algorithms for spatial databases

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Published:01 June 1998Publication History

ABSTRACT

Two new spatial join operations, distance join and distance semi-join, are introduced where the join output is ordered by the distance between the spatial attribute values of the joined tuples. Incremental algorithms are presented for computing these operations, which can be used in a pipelined fashion, thereby obviating the need to wait for their completion when only a few tuples are needed. The algorithms can be used with a large class of hierarchical spatial data structures and arbitrary spatial data types in any dimensions. In addition, any distance metric may be employed. A performance study using R-trees shows that the incremental algorithms outperform non-incremental approaches by an order of magnitude if only a small part of the result is needed, while the penalty, if any, for the incremental processing is modest if the entire join result is required.

References

  1. 1.W. G. Aref and H. Samet. The spatial filter revisited. Proc. of 6th international Symposium on Spatial Data Handling, pp. 190-208, Edinburgh, Scotland, September 1994.]]Google ScholarGoogle Scholar
  2. 2.17. Battling and K. Hinrichs. Probabilistic analysis of an algorithm for solving the k-dimensional all-nearest-neighbors problem by projection. BIT, 3 i(4):558-565, 1991.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. 3.R. J. Bayardo and D. E Miranker. Processing queries for first few answers. In Proc. of5th CIKM, pp. 45-52, Rockville, MD, November 1996.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. 4.L. Becker, K. Hinrichs, and U. Finke. A new algorithm for computing joins with grid files. Proc. of 9th IEEE Int. Conf on Data Engineering, pp. 190-197, Vienna, Austria, April 1993.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. 5.N. Beckmann, H. P. Kriegel, R. Schneider, and B. Seeger. The R*-tree: an efficient and robust access method for points and rectangles. Proc. of ACM SIGMOD, pp. 322-331, Atlantic City, NJ, June 1990.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. 6.S. N. Bespamyatnikh. An optimal algorithm for closest pair maintenance. Proc. of 11 th Symp. on Computational Geometry, pp. 152-161, Vancouver, British Columbia, June 1995.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. 7.3". Brinkhoff, H. P. Kriegel, R. Schneider, and B. Seeger. Multistep processing of spatial joins. Proc. ofA CM SIGMOD, pp. ! 97-208, Minneapolis, MN, June 1994.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. 8.T. Brinkhoff, H. E Kriegel, and B. Seeger. Efficient processing of spatial joins using R-trees. Proc. ofA CM SIGMOD, pp. 237-246, Washington, DC, May I993.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. 9.Bureau of the Census. Tiger~Line precensusfiles. Washington, DC, 1989.]]Google ScholarGoogle Scholar
  10. 10.M. J. Carey and D. Kossmann. On saying "enough already!" in SQL. Proc. ofA CM SIGMOD, pp. 219-230, Tucson, AZ, May 1997.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. 11.K. L. Clarkson. Fast algorithm for the all nearest neighbors problem. Proc. of 24th IEEE Syrup. on the Foundations of Computer Science, pp. 226-232, Tucson, November 1983.]]Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. 12.D. Comer. The ubiquitous B-tree. A CM Computing Surveys, 11(2): 121-137, June 1979.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. 13.M.L. Fredman, R. Sedgewick, D. D. Sleator, and R. E. Tarjan. The pairing heap: A new form of self-adjusting heap. Algorithmica, 1 ( I ): 111-129, 1986.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. 14.O. Gtinther. Efficient computation of spatial joins. Proc. of 9th IEEE Int. Conf. on Data Engineering, pp. 50-59, Vienna, Austria, April 1993.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. 15.A. Guttman. R-trees: a dynamic index structure for spatial searching. Proc. ofACM SIGMOD, pp. 47-57, Boston, MA, June 1984.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. 16.J. M. tlellerstein, E J. Haas, and H. Wang. Online aggregation. Proc. ofACM SIGMOD, pp. 171-182, Tucson, AZ, May 1997.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. 17.A. Henrich. A distance-scan algorithm for spatial access structures. Proc. of 2nd ACM Workshop on GIS, pp. 136-143, Gaithersburg, MD, December 1994.]]Google ScholarGoogle Scholar
  18. 18.G. R. Hjaltason and H. Samet. Ranking in spatial databases. Advances in Spatial Databases ~ 4th Int. Symp., SSD '95, pp. 83-95, Portland, ME, August 1995. (Also Springer-Verlag Lecture Notes in Computer Science 951).]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. 19.E. Hoel and H. Samct. Data-parallel spatial join algorithms, Proc. of 23rd Int. Conf on Parallel Processing, pp. 227-234, St. Charles, IL, August 1994.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. 20.Y.-W. Huang, N. Jing, and E. A. Rundensteiner. A cost model for estimating the performance of spatial joins using r-trees. Proc. of 9th int. Conf on Scientific and Statistical Database Management, pp. 30-38, Olympia, WA, August 1997.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. 21.Y.-W. Huang, N. Jing, and E. A. Rundensteiner. Spatial joins using r-trees: breadth-first traversal with global optimizations. Proc. of 23rd VLDB Conf., pp. 396--405, Athens, Greece, August 1997.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. 22.M. Kitsuregawa, L. Harada, and M. Takagi. Join strategies on k-d-tree indexed relations. Proc. of 5th IEEE Int. Conf. on Data Engineering, pp. 85-93, Los Angeles, February 1989.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. 23.M. L. Lo and C. V. Ravishankar. Spatial joins using seeded trees. Proc. of A CM SIGMOD, pp. 209-220, Minneapolis, MN, June 1994.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. 24.D. Lomet and B. Salzberg. A robust multi-attribute search structure. Proc. of the 5th IEEE Int. Conf on Data Engineering, pp. 296-304, Los Angeles, February 1989.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. 25.D. Rotem. Spatial join indices. Proc. of 7th Int. Conf on Data Engineering, pp. 500-509, Kobe, Japan, April 1991.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. 26.N. Roussopoulos, S. Kelley, and E Vincent. Nearest neighbor queries. Proc. of ACM SIGMOD, pp. 71-79, San Jose, CA, May 1995.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. 27.H. Samet. Applications of spatial data structures: Computer graphics, image processing, and GIS. Addison-Wesley, Reading, MA, 1990.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. 28.H. Samet. The design and analysis of spatial data structures. Addison-Wesley, Reading, MA, 1990.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. 29.B. Seeger and H. P. Kriegel. The buddy-tree: an efficient and robust access method for spatial data base systems. Proc. of 16th VLDB Conf., pages 590-601, Brisbane, Australia, August 1990.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. 30.J. C. Sharer and R. Agrawal. Parallel algorithms for highdimensional proximity joins. Proc. of 23rd VLDB Con(., pp. 176-185, Athens, Greece, August 1997.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. 31.H. W. Six and D. Wood. Counting and reporting intersections of d-ranges. IEEE Transactions on Computers, 31 (3):!81- 187, March 1982.]]Google ScholarGoogle Scholar
  32. 32.P. M. Vaidya. An O(n log n) algorithm for the all-nearestneighbor problem. Discrete & Computational Geometry, 4(2):101-115, 1989.]]Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. 33.T. L. Wang and D. Shasha. Query processing for distance metrics. Proc. of 16th VLDB Conf., pages 602-613, Brisbane, Australia, August I990.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. 34.A. N. Wilschut and E M. G. Apers. Dataflow query execution in a parallel main-memory environment. Proc. of 1st Int. Con(. on Parallel and Distributed information Systems, pp. 68-77, Miami, FL, December 1991.]] Google ScholarGoogle ScholarDigital LibraryDigital Library

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                cover image ACM Conferences
                SIGMOD '98: Proceedings of the 1998 ACM SIGMOD international conference on Management of data
                June 1998
                599 pages
                ISBN:0897919955
                DOI:10.1145/276304

                Copyright © 1998 ACM

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                • Published: 1 June 1998

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