Selective materialization: An efficient method for spatial data cube construction

  • Jiawei Han
  • Nebojsa Stefanovic
  • Krzysztof Koperski
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1394)


On-line analytical processing (OLAP) has gained its popularity in database industry. With a huge amount of data stored in spatial databases and the introduction of spatial components to many relational or object-relational databases, it is important to study the methods for spatial data warehousing and on-line analytical processing of spatial data. In this paper, we study methods for spatial OLAP, by integration of nonspatial on-line analytical processing (OLAP) methods with spatial database implementation techniques. A spatial data warehouse model, which consists of both spatial and nonspatial dimensions and measures, is proposed. Methods for computation of spatial data cubes and analytical processing on such spatial data cubes are studied, with several strategies proposed, including approximation and partial materialization of the spatial objects resulted from spatial OLAP operations. Some techniques for selective materialization of the spatial computation results are worked out, and the performance study has demonstrated the effectiveness of these techniques.


Data warehouse data mining on-line analytical processing (OLAP) spatial databases spatial data analysis spatial OLAP 


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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Jiawei Han
    • 1
  • Nebojsa Stefanovic
    • 1
  • Krzysztof Koperski
    • 1
  1. 1.School of Computing ScienceSimon Fraser UniversityBurnabyCanada

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