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.
- 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 Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 9.Bureau of the Census. Tiger~Line precensusfiles. Washington, DC, 1989.]]Google Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 12.D. Comer. The ubiquitous B-tree. A CM Computing Surveys, 11(2): 121-137, June 1979.]] Google ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 15.A. Guttman. R-trees: a dynamic index structure for spatial searching. Proc. ofACM SIGMOD, pp. 47-57, Boston, MA, June 1984.]] Google ScholarDigital Library
- 16.J. M. tlellerstein, E J. Haas, and H. Wang. Online aggregation. Proc. ofACM SIGMOD, pp. 171-182, Tucson, AZ, May 1997.]] Google ScholarDigital Library
- 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 Scholar
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 25.D. Rotem. Spatial join indices. Proc. of 7th Int. Conf on Data Engineering, pp. 500-509, Kobe, Japan, April 1991.]] Google ScholarDigital Library
- 26.N. Roussopoulos, S. Kelley, and E Vincent. Nearest neighbor queries. Proc. of ACM SIGMOD, pp. 71-79, San Jose, CA, May 1995.]] Google ScholarDigital Library
- 27.H. Samet. Applications of spatial data structures: Computer graphics, image processing, and GIS. Addison-Wesley, Reading, MA, 1990.]] Google ScholarDigital Library
- 28.H. Samet. The design and analysis of spatial data structures. Addison-Wesley, Reading, MA, 1990.]] Google ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
- 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 Scholar
- 32.P. M. Vaidya. An O(n log n) algorithm for the all-nearestneighbor problem. Discrete & Computational Geometry, 4(2):101-115, 1989.]]Google ScholarDigital Library
- 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 ScholarDigital Library
- 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 ScholarDigital Library
Index Terms
- Incremental distance join algorithms for spatial databases
Recommendations
Incremental distance join algorithms for spatial databases
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 ...
Adaptive and Incremental Processing for Distance Join Queries
A spatial distance join is a relatively new type of operation introduced for spatial and multimedia database applications. Additional requirements for ranking and stopping cardinality are often combined with the spatial distance join in online query ...
New plane-sweep algorithms for distance-based join queries in spatial databases
Efficient and effective processing of the distance-based join query (DJQ) is of great importance in spatial databases due to the wide area of applications that may address such queries (mapping, urban planning, transportation planning, resource ...
Comments