Approximate Processing of Multiway Spatial Joins in Very Large Databases

  • Dimitris Papadias
  • Dinos Arkoumanis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2287)


Existing work on multiway spatial joins focuses on the retrieval of all exact solutions with no time limit for query processing. Depending on the query and data properties, however, exhaustive processing of multiway spatial joins can be prohibitively expensive due to the exponential nature of the problem. Furthermore, if there do not exist any exact solutions, the result will be empty even though there may exist solutions that match the query very closely. These shortcomings motivate the current work, which aims at the retrieval of the best possible (exact or approximate) solutions within a time threshold, since fast retrieval of approximate matches is the only way to deal with the ever increasing amounts of multimedia information in several real time systems. We propose various techniques that combine local and evolutionary search with underlying indexes to prune the search space. In addition to their usefulness as standalone methods for approximate query processing, the techniques can be combined with systematic search to enhance performance when the goal is retrieval of the best solutions.


Local Search Query Processing Constraint Satisfaction Problem Evolutionary Search Query Graph 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [BKS93]
    Brinkhoff, T., Kriegel, H., Seeger B. Efficient Processing of Spatial Joins Using R-trees. ACM SIGMOD, 1993.Google Scholar
  2. [BKSS90]
    Beckmann, N., Kriegel, H. Schneider, R., Seeger, B. The R*-tree: an Efficient and Robust Access Method for Points and Rectangles. ACM SIGMOD, 1990.Google Scholar
  3. [BT96]
    Blickle, T., Thiele, L. A Comparison of Selection Schemes used in Genetic Algorithms. TIK-Report No. 11, ETH, Zurich, 1996.Google Scholar
  4. [CA93]
    Crawford, J., Auton, L. Experimental Results on the Crossover Point in Satisfiability Problems. AAAI, 1993.Google Scholar
  5. [CFG+98]
    Clark, D., Frank, J., Gent, I., MacIntyre, E., Tomov, N., Walsh, T. Local Search and the Number of Solutions. Constraint Programming, 1998.Google Scholar
  6. [CSY87]
    Chang, S, Shi, Q., Yan C. Iconic Indexing by 2-D String. IEEE PAMI 9(3), 413–428, 1987.Google Scholar
  7. [DM94]
    Dechter R., Meiri I. Experimental Evaluation of preprocessing algorithms for constraint satisfaction problems. Artificial Intelligence, 68: 211–241, 1994.zbMATHCrossRefGoogle Scholar
  8. [DTW+94]
    Davenport, A., Tsang, E., Wang, C., Zhu, K. GENET: A Connectionist Architecture for Solving Constraint Satisfaction Problems by Iterative Improvement. AAAI, 1994.Google Scholar
  9. [G84]
    Guttman, A. R-trees: A Dynamic Index Structure for Spatial Searching. ACM SIGMOD, 1984.Google Scholar
  10. [G86]
    Grefenstette, J. Optimization of Control Parameters for Genetic Algorithms. IEEE Trans. on Systems, Man and Cybernetics, 16 (1), 1986.Google Scholar
  11. [G89]
    Goldberg, D. Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading, Mass., 1989.zbMATHGoogle Scholar
  12. [GL97]
    Glover F., Laguna, M. Tabu Search. Kluwer, London, 1997.Google Scholar
  13. [H75]
    Holland, J. Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, Michigan, 1975.Google Scholar
  14. [LH92]
    Lee, S, Hsu, F. Spatial Reasoning and Similarity Retrieval of Images using 2D C-Strings Knowledge Representation. Pattern Recognition, 25(3), 305–318, 1992.CrossRefMathSciNetGoogle Scholar
  15. [LYC92]
    Lee, S, Yang, M, Chen, J. Signature File as a Spatial Filter for Iconic Image Database. Journal of Visual Languages and Computing, 3, 373–397, 1992.CrossRefGoogle Scholar
  16. [MJP+92]
    Minton, S. Johnston, M., Philips, A., Laird P. Minimizing Conflicts: A Heuristic Repair Method for Constraint Satisfaction and Scheduling Problems. Artificial Intelligence 58(1–3), 161–205, 1992.zbMATHCrossRefMathSciNetGoogle Scholar
  17. [MP99]
    Mamoulis, N, Papadias, D., Integration of Spatial Join Algorithms for Processing Multiple Inputs. ACM SIGMOD, 1999.Google Scholar
  18. [PA]
    Papadias, D., Arkoumanis D. Search Algorithms for Multiway Spatial Joins. To appear in the International Journal of Geographic Information Science (IJGIS). Available at:
  19. [PF97]
    Petrakis, E., Faloutsos, C. Similarity Searching in Medical Image Databases. IEEE TKDE, 9 (3) 435–447, 1997.Google Scholar
  20. [PLC00]
    Park, H-H., Lee, J-Y., Chung, C-W. Spatial Query Optimization Utilizing Early Separated Filter and Refinement Strategy. Information Systems 25(1): 1–22, 2000.zbMATHCrossRefGoogle Scholar
  21. [PMK+99]
    Papadias, D., Mantzourogiannis, M., Kalnis, P., Mamoulis, N., Ahmad, I. Content-Based Retrieval Using Heuristic Search. ACM SIGIR, 1999.Google Scholar
  22. [PMT99]
    Papadias, D., Mamoulis, N., Theodoridis, Y. Processing and Optimization of Multiway Spatial Joins Using R-trees. ACM PODS, 1999.Google Scholar
  23. [TSS98]
    Theodoridis, Y., Stefanakis, E., Sellis, T., Cost Models for Join Queries in Spatial Databases, ICDE, 1998.Google Scholar
  24. [ZSI01]
    Zhu, H, Su, J, Ibarra, O. On Multi-way Spatial Joins with Direction Predicates. SSTD, 2001.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Dimitris Papadias
    • 1
  • Dinos Arkoumanis
    • 2
  1. 1.Department of Computer ScienceHong Kong University of Science and TechnologyClear Water BayHong Kong
  2. 2.Dept of Electrical and Computer EngineeringNational Technical University of AthensGreece

Personalised recommendations