Journal of Computer Science and Technology

, Volume 17, Issue 5, pp 625–635 | Cite as

Compressed data cube for approximate OLAP query processing



Approximate query processing has emerged as an approach to dealing with the huge data volume and complex queries in the environment of data warehouse. In this paper, we present a novel method that provides approximate answers to OLAP queries. Our method is based on building a compressed (approximate) data cube by a clustering technique and using this compressed data cube to provide answers to queries directly, so it improves the performance of the queries. We also provide the algorithm of the OLAP queries and the confidence intervals of query results. An extensive experimental study with the OLAP council benchmark shows the effectiveness and scalability of our cluster-based approach compared to sampling.


OLAP approximate query processing clustering data cube 


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  1. [1]
    Gray J, Bosworth A, Layman A, Pirahesh H. DataCube: A relational aggragation operator generalizing Group-By, Gross-Tab, and Sub Totals. InProc. 12th ICDE, Neworleans, Louisiana, USA, 1996, pp.152–159.Google Scholar
  2. [2]
    Sarawagi S, Stonebraker M. Efficient organization of large multidimensional arrays. InProc of ICDE, Houston, Texas, USA, 1994, pp.328–336.Google Scholar
  3. [3]
    Han J, Kambr M. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, 2000.Google Scholar
  4. [4]
    The OLAP Council. The OLAP benchmark. http://www.olapcouncil.orgGoogle Scholar
  5. [5]
    Barbara D, DuMouchel W, Faloutsos Cet al., The New Jersey data reduction report.IEEE Data Engincering Bulletin, 1997, 20(4): 3–45.Google Scholar
  6. [6]
    Acharya S, Gibbons P B, Poosala V, Ramaswamy S. Join Synopses for approximate query answering. InSIGMOD’1999, Philadelphia, Pennsylvania, USA, 1999, pp.275–286.Google Scholar
  7. [7]
    Vitter J S, Wang M. Approximate computation of multidimensional aggregates of sparse data using wavelets. InSIGMOD’1999, Philadelphia, Pennsylvania, USA, 1999, pp.193–204.Google Scholar
  8. [8]
    Shanmugasundaram J, Fayyad U, Bradley P S. Compressed Data Cubes for OLAP Aggregate Query Approximation on Continuous Dimensions. InKDD’1999 San Diego, California, USA, 1999, pp.223–232.Google Scholar
  9. [9]
    Jagadish H V, Madar J, Ng R T. Semantic Compression and Pattern Extraction with Fascicles. InVLDB’1999, Edinburgh, Scotland, 1999, pp.186–198.Google Scholar
  10. [10]
    Babu S, Garofalakis M, Rastogi R. SPARTAN: A model-based semantic compression system for massive data tables. InSIGMOD’2001, Santa Barbara, California, USA, 2001, pp.283–294.Google Scholar
  11. [11]
    Li J, Rotem D, Srivastava J. Aggregation algorithms for very large compressed data warehouses. InVLDB’1999, Edinburgh, Scotland, 1999, pp.651–662.Google Scholar

Copyright information

© Science Press, Beijing China and Allerton Press Inc. 2002

Authors and Affiliations

  1. 1.School of InformationRenmin University of ChinaBeijingP.R. China

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