Patchwork — A query-driven locally adaptive data space partitioning

  • Gisbert Dröge
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 884)


The major goal of spatial access methods in query optimization is to deliver the exact or a minimal superset of the result set and to perform this task at minimal cost. We present a clustering spatial access method that directly delivers exact result sets. Minimal cost is guaranteed through a cost-based adaptation strategy that dynamically determines and realizes storage clusters best suited for a set of spatial range queries.

We introduce a tessalation of the data space which allows irregular, arbitrary small and large patches. Such patches can be adapted to query ranges in order to answer queries at minimal cost. The adaptation cost is kept small by performing only local repartitioning. Thus only a small number of neighbouring patches are merged or split during an adaptation step. The directory part has to be simple to perform range queries at minimal cost and to allow frequent adaptation updates at moderate cost. An implementation and evaluation in a database prototype system environment is under developement.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Norbert Beckmann, Hans-Peter Kriegel, Ralf Schneider, and Bernhard Seeger. The R*-tree: An Efficient and Robust Access Method for Points and Rectangles. In Proc. of ACM SIGMOD Conf., pages 322–331, June 1990.Google Scholar
  2. 2.
    Uwe Deppisch, Hans-Bernd Paul, and Hans-Jörg Schek. A Storage System for Complex Objects. In Klaus Dittrich and Umeshwar Dayal, editors, International Workshop on Object-Oriented Database Systems, Proceedings, pages 183–195. IEEE Computer Society Press, September 1986.Google Scholar
  3. 3.
    Gisbert Dröge and Hans-Jörg Schek. Query-adaptive data space partitioning using variable-size storage clusters. In David Abel and Beng Chin Ooi, editors, Advances in Spatial Databases, Third Symposium, SSD '93, Proceedings, volume 692 of LNCS, pages 337–356. Springer Verlag, June 1993.Google Scholar
  4. 4.
    Gisbert Dröge, Hans-Jörg Schek, and Andreas Wolf. Extensibility in DASDBS. Informatik Forschung und Entwicklung, 5:162–176, 1990. (in German).Google Scholar
  5. 5.
    O. Günther and A. Buchmann. Research Issues in Spatial Databases. ACM SIG-MOD RECORD, 19(4):61–68, December 1990.Google Scholar
  6. 6.
    Oliver Günther. The Design of the Cell Tree: An Object-Oriented Index Structure for Geometric Databases. In Proc. of the 5th IEEE Int. Conf. on Data Engineering, pages 598–605, February 1989.Google Scholar
  7. 7.
    Oliver Günther and Hartmut Noltemeier. Spatial Database Indices for Large Extended Objects. In Proc. of the 7th IEEE Int. Conf. on Data Engineering, pages 520–526, April 1991.Google Scholar
  8. 8.
    A. Guttman. R-trees: A Dynamic Index Structure for Spatial Searching. ACM SIGMOD Proceedings of Annual Meeting, 14(2):47–57, June 1984.Google Scholar
  9. 9.
    Hans-Peter Kriegel, Holger Horn, and Michael Schiwietz. The Performance of Object Decomposition Techniques for Spatial Query Processing. In O. Günther and H.-J. Schek, editors, Advances in Spatial Databases, 2nd Symposium, SSD '91, Proceedings, volume 525 of LNCS, pages 257–276. Springer Verlag, August 1991.Google Scholar
  10. 10.
    Samuel J. Leffler, Marshall Kirk McKusick, Michael J. Karels, and John S. Quaterman. The Design and Implementation of the 4.3BSD Unix Operating System. Addison-Wesley Publishing Company, October 1990.Google Scholar
  11. 11.
    Hongjun Lu and Beng-Chin Ooi. Spatial Indexing: Past and Future. Bulletin of the Technical Committee on Data Engineering, 19(3):16–21, September 1993.Google Scholar
  12. 12.
    Jürg Nievergelt, Hans Hinterberger, and K.C. Sevcik. The Grid File: An Adaptable, Symmetric Multikey File Structure. ACM Trans. on Database Systems, 9(1):38–71, March 1984.Google Scholar
  13. 13.
    Heinz-Bernhard Paul, Hans-Jörg Schek, Marc H. Scholl, Gerhard Weikum, and Uwe Deppisch. Architecture and Implementation of the Darmstadt Database Kernel System. In Proc. of the 1981 ACM SIGMOD Conference, San Francisco, pages 196–207, May 1987.Google Scholar
  14. 14.
    John T. Robinson. The K-D-B-Tree: A Search Structure for Large Multidimensional Dynamic Indexes. In Proc. of ACM SIGMOD Conf., pages 10–18, 1981.Google Scholar
  15. 15.
    Hans-Jörg Schek and Walter Waterfeld. A database kernel system for geoscientific applications. In Proc. 2nd Int. Symp. on Spatial Data Handling, pages 273–288, 1986.Google Scholar
  16. 16.
    Hans-Jörg Schek and Andreas Wolf. Cooperation between Autonomous Operation Services and Object Database Systems in a Heterogeneous Environment. In DavidK. Hsiao, Erich J. Neuhold, and Ron Sacks-Davis, editors, Proc. of IFIP DS-5 Semantics of Interoperable Database Systems, pages 255–281. Elsevier Science Publishers B.V. (North-Holland), November 1992.Google Scholar
  17. 17.
    Berhard Seeger and Hans-Peter Kriegel. Techniques for Design and Implementation of Efficient Spatial Access Methods. In Proc. of the 14th VLDB Conf., pages 360–371, 1988.Google Scholar
  18. 18.
    Timos Sellis, Nick Roussopoulos, and Christos Faloutsos. The R+-Tree: A Dynamic Index Structure for Multi-Dimensional Objects. In Proc. of the 13th VLDB Conference, Brighton, pages 507–518, 1987.Google Scholar
  19. 19.
    Gerhard Weikum. Set-Oriented Disk Access to Large Complex Objects. In Proc. of the 5th IEEE Int. Conf. on Data Engineering, pages 426–433, February 1989.Google Scholar
  20. 20.
    Andreas Wolf. The DASDBS GEO-Kernel, Concepts, Experiences, and the Second Step. In Alejandro P. Buchman, Oliver Günther, Terry R. Smith, and Yuan-F. Wang, editors, Design and Implementation of Large Spatial Databases, First Symposium SSD '89, Proceedings, LNCS 409, pages 67–88. Springer Verlag, July 1989.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • Gisbert Dröge
    • 1
  1. 1.Information Systems - DatabasesETH ZürichZürichSwitzerland

Personalised recommendations