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An Approach to Enabling Spatial OLAP by Aggregating on Spatial Hierarchy

  • Long Zhang
  • Ying Li
  • Fangyan Rao
  • Xiulan Yu
  • Ying Chen
  • Dong Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2737)

Abstract

Investigation shows that a huge number of spatial data exists in current business databases. Traditional data warehousing and OLAP, however, could not exploit the spatial information to get deep insight into the business data in decision making. In this paper, we propose a novel approach to enabling spatial OLAP by aggregating on the spatial hierarchy. A spatial index mechanism is employed to derive the spatial hierarchy for pre-aggregation and materialization, which in turn are leveraged by the OLAP system to efficiently answer spatial OLAP queries. Our prototype system shows that the proposed approach could be integrated easily into the existing data warehouse and OLAP systems to support spatial analysis. Preliminary experiment results are also presented.

Keywords

Spatial Object Spatial Aggregation Spatial Index Minimum Bound Rectangle Query Window 
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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References

  1. 1.
    Baralis, E., Paraboschi, S., Teniente, E.: Materialized View Selection in a Multidimensional Database. In: Proceedings of VLDB Conference (1997)Google Scholar
  2. 2.
    Colossi, N., Malloy, W., Reinwald, B.: Relational extensions for OLAP. IBM SYSTEMS JOURNAL 41(4) (2002)Google Scholar
  3. 3.
    Adler, D.W.: DB2 Spatial Extender - Spatial data within the RDBMS. In: Proceedings of VLDB Conference, pp. 687–690 (2001)Google Scholar
  4. 4.
    Daratech: Geographic Information Systems Markets and Opportunities. Daratech, Inc. (2000)Google Scholar
  5. 5.
    Gray, J., Chaudhuri, S., Bosworth, A., et al.: Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals. Data Mining and Knowledge Discovery Journal 1, 29–53 (1997)CrossRefGoogle Scholar
  6. 6.
    Gaede, V., Gnther, O.: Multidimensional Access Methods. ACM Computing Surveys (1997)Google Scholar
  7. 7.
    Gupta, H.: Selection of Views to Materialize in a Data Warehouse. In: Proceedings of International Conference on Database Theory (1997)Google Scholar
  8. 8.
    Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann Publisher, Inc., San Francisco (2001)Google Scholar
  9. 9.
    Han, J., Stefanovic, N., Koperski, K.: Selective Materialization: An Efficient Method for Spatial Data Cube Construction. In: Wu, X., Kotagiri, R., Korb, K.B. (eds.) PAKDD 1998. LNCS, vol. 1394. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  10. 10.
    Java OLAP Interface (JOLAP), version 0.85. Java Community Process (May 2002), Available at http://jcp.org/en/jsr/detail?id=069
  11. 11.
    Kothui, R.K.V., Ravada, S., Abugov, D.: Quadtree and R-tree Indexes in Oracle Spatial: A Comparison using GIS Data. In: ACM SIGMOD Conference (2002)Google Scholar
  12. 12.
    Papadias, D., Kalnis, P., Zhang, J., Tao, Y.: Efficient OLAP operations in spatial data warehouses. Technical Report HKUST-CS01-01 (January 2001)Google Scholar
  13. 13.
    Papadias, D., Tao, Y., Kalnis, P., Zhang, J.: Indexing Spatio-Temporal Data Warehouses. In: Proceedings of International Conference on Data Engineering (2002)Google Scholar
  14. 14.
    Stefanovic, N., Han, J., Koperski, K.: Object-Based Selective Materialization for Efficient Implementation of Spatial Data Cubes. TKDE 12(6), 938–958 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Long Zhang
    • 1
  • Ying Li
    • 1
  • Fangyan Rao
    • 1
  • Xiulan Yu
    • 1
  • Ying Chen
    • 1
  • Dong Liu
    • 1
  1. 1.IBM China Research LaboratoryBeijingP.R. China

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