Monet and its geographical extensions: A novel approach to high performance GIS processing

  • Peter A. Boncz
  • Wilko Quak
  • Martin L. Kersten
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1057)


We describe Monet, a novel database system, designed to get maximum performance out of today's workstations and symmetric multiprocessors.

Monet is a type- and algebra-extensible database system using the Decomposed Storage Model (DSM) and employing shared memory parallelism. It applies purely main-memory algorithms for processing and uses OS virtual memory primitives for handling large data. Monet provides many options in memory management and virtual-memory clustering strategies to optimize access to its tables.

We discuss how these unusual features impacted the design, implementation and performance of a set of GIS extension modules, that can be loaded at runtime in Monet, to obtain a functional complete GIS server.

The validity of our approach is shown by excellent performance figures on both the Regional and National Sequoia storage benchmark.


Main Memory Memory Management Cache Strategy Virtual Memory Access Path 
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.


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  1. 1.
    P. M. G. Apers, C. A. van den Berg, J. Flokstra, P. W. P. J. Grefen, M. L. Kersten, and A. N. Wilschut. PRISMA/DB: A parallel main memory relational DBMS. IEEE Trans. on Knowledge and Data Eng., 4(6):541, December 1992.Google Scholar
  2. 2.
    P. A. Boncz and M. L. Kersten. Monet: An impressionist sketch of an advanced database system. In Proc. IEEE BIWIT workshop, San Sebastian (Spain)., July 1995.Google Scholar
  3. 3.
    T. Brinkhoff, H. Kriegel, R. Schneider, and B. Seeger. Multi-step processing of spatial joins. In 23 ACM SIGMOD Conf. on the Management of Data, pages 197–208, June 1994.Google Scholar
  4. 4.
    G. Copeland and S. Khoshafian. A decomposition storage model. In Proc. ACM SIGMOD Conf., page 268, Austin, TX, May 1985.Google Scholar
  5. 5.
    David J. DeWitt, Navin Kabra, Jun Luo, Jignesh M. Patel, and Jie-Bing Yu. Client-server Paradise. In Proceedings of the 20th VLDB Conference, Santiago, Chile., pages 558–569, September 1994.Google Scholar
  6. 6.
    et al. Carey,M. and DeWitt,D. The EXODUS extensible DBMS project: An overview. In In 'Readings in Object-Oriented Database Systems.Google Scholar
  7. 7.
    et al. G.Gardarin and M.Jean-Noël. Sabrina, a relational database system developed in a research environment. In Technology and Sciences of Informatics. AFCET-Gauthier Villard — John Willey and Sons Ltd., 1987.Google Scholar
  8. 8.
    et al. Neuhold, E. and Stonebraker, M. Future directions in DBMS research. ACM SIGMOD RECORD, 18(1), March 1989.Google Scholar
  9. 9.
    G. Graefe. Encapsulation of parallelism in the volcano query processing system. In 19 ACM SIGMOD Conf. on the Management of Data, Atlantic City, May 1990.Google Scholar
  10. 10.
    R. H. Guting. Gral: An extensible relational database system for geometric applications.Google Scholar
  11. 11.
    D. Lieuwen H. V. Jagadish, R. Rastogi, A. Silberschatz, and S. Sudarshan. Dalí: A high performance main memory storage manager. In Proceedings of the 20th VLDB Conference, Santiago, Chile., pages 48–59, September 1994.Google Scholar
  12. 12.
    M. Holsheimer, M. L. Kersten, and A. Siebes. Data Surveyor: searching for nuggets in parallel. In Knowledge Discovery in Databases. MIT Press, Cambridge, MA, USA, 1995.Google Scholar
  13. 13.
    S. Khoshafian, G. Copeland, T. Jagodits, H. Boral, and P. Valduriez. A query processing strategy for the decomposed storage model. In Proc. IEEE CS Intl. Conf. No. 3 on Data Engineering, Los Angeles, February 1987.Google Scholar
  14. 14.
    T. J. Lehman and M. J. Carey. A study of index structures for main memory database management systems. In Proceedings of the 12th VLDB Conference, Kyoto, August 1986.Google Scholar
  15. 15.
    H. Samet. The Design and Analysis of Spatial Data Structures. Addison Wesley, 1990.Google Scholar
  16. 16.
    M. Stonebraker. Operating system support for database management. Communications of the ACM, 14(7), July 1981.Google Scholar
  17. 17.
    M. Stonebraker. Inclusion of new types in relational database systems. In Proc. IEEE CS Intl. Conf. No. 2 on Data Engineering, Los Angeles, February 1986.Google Scholar
  18. 18.
    M. Stonebraker, J. Frew, K. Gardels, and J. Meredith. The Sequoia 2000 storage benchmark. In 19 ACM SIGMOD Conf. on the Management of Data, Washington, DC, May 1993.Google Scholar
  19. 19.
    M. Stonebraker and G. Kemnitz. The POSTGRES next-generation database management system. Comm. of the ACM, Special Section on Next-Generation Database Systems, 34(10):78, October 1991.Google Scholar
  20. 20.
    C. A. van den Berg and M. L. Kersten. An analysis of a dynamic query optimisation scheme for different data distributions. In J. Freytag, D. Maier, and G. Vossen, editors, Advances in Query Processing, pages 449–470. Morgan-Kaufmann, San Mateo, CA, 1994.Google Scholar
  21. 21.
    Seth J. White and David J. DeWitt. Quickstore: A high performance mapped object store. In ACM SIGMOD Conf. on the Management of Data, pages 395–406, May 1994.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Peter A. Boncz
    • 1
  • Wilko Quak
    • 1
  • Martin L. Kersten
    • 1
  1. 1.University of Amsterdam, CWINetherland

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