Journal of Computer Science and Technology

, Volume 19, Issue 3, pp 302–308 | Cite as

Incremental maintenance of quotient cube based on galois lattice

Knowledge and Data Processing


Data cube computation is a well-known expensive operation and has been studied extensively. It is often not feasible to compute a complete data cube due to the huge storage requirement. Recently proposed quotient cube addressed this fundamental issue through a partitioning method that groups cube cells into equivalent partitions. The effectiveness and efficiency of the quotient cube for cube compression and computation have been proved. However, as changes are made to the data sources, to maintain such a quotient cube is non-trivial since the equivalent classes in it must be split or merged. In this paper, incremental algorithms are designed to update existing quotient cube efficiently based on Galois lattice. Performance study shows that these algorithms are efficient and scalable for large databases.


Galois lattice quotient cube incremental maintenance 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Lakshamanan L V Set al. Quotient cube: How to summarize the semantics of a data cube. InProc. the 28th Int. Conf. Very Large Databases (VLDB), Hong Kong, 2002, pp.778–789.Google Scholar
  2. [2]
    Godin Ret al. Incremental concept formation algorithms based on Galois lattices.Computational Intelligence, 1991, 11(2): 246–267.CrossRefGoogle Scholar
  3. [3]
    Davey B Aet al. Introduction to Lattices and Order. Cambridge University Press, 1990.Google Scholar
  4. [4]
    Wille Ret al. Concept lattices and conceptual knowledge systems.Computers & Mathematics with Applications, 23(6–9): 493–515.Google Scholar
  5. [5]
    Gray Jet al. Data cube: A relational aggregation operator generalizing group-by, cross-tab, and sub-total. InProc. 1996 Int. Conf. Data Engineering (ICDE), New Orleans, Louisiana, 1996, pp.152–159.Google Scholar
  6. [6]
    Wang Wet al. Condensed cube: An effective approach to reducing data cube size. InProc. 2002 Int. Conf. Data Engineering (ICDE), San Jose, California, 2002, pp.155–165.Google Scholar
  7. [7]
    Agarwal Set al. On the computation of multidimensional aggregates. InProc. the 22nd Int. Conf. Very Large Databases (VLDB), Bombay, India, 1996, pp.522–531.Google Scholar
  8. [8]
    Zhao Yet al. An array-based algorithm for simultaneous multidimensional aggregates. InProc. ACM-SIGMOD Int. Conf. Management of Data, Tucson, Arizona, 1997, pp.159–170.Google Scholar
  9. [9]
    Harinarayan Vet al. Implementing data cubes efficiently. InProc. ACM-SIGMOD Int. Conf. Management of Data, Montreal, Canada, 1996, pp.208–219.Google Scholar
  10. [10]
    Shanmugasundaram Jet al. Compressed data cubes for OLAP aggregate query approximation on continuous dimensions. InProc. ACM-SIGKDD Int. Conf. Management of Data, San Diego, California, 1999, pp.223–232.Google Scholar
  11. [11]
    Barbara Det al. Quasi-cubes: Exploiting approximation in multidimensional databases.SIGMOD Record, 1997, 26: 12–17.CrossRefGoogle Scholar
  12. [12]
    Ross K, Srivastava D. Fast computation of sparse data-cubes. InProc. the 23rd Int. Conf. Very Large Databases (VLDB'97), Athens, Greece, 1997, pp.116–125.Google Scholar
  13. [13]
    Beyer Ket al. Bottom-up computation of sparse and iceberg cubes. InProc. ACM-SIGMOD Int. Conf. Management of Data, Pennsylvania, USA, 1999, pp.359–370.Google Scholar
  14. [14]
    Sismanis Yet al. Dwarf: Shrinking the petacube. InProc. ACM-SIGMOD Int. Conf. Management of Data, Wisconsin, USA, 2002, pp.464–475.Google Scholar
  15. [15]
    Labio W J, Yang U, Cui Yet al. Performance issues in incremental warehouse maintenance. InProc. the 26th Int. Conf. Very Large Databases (VLDB'00), Cairo, Egypt, 2000, pp.461–472.Google Scholar
  16. [16]
    Mumick I S, Quass D, Mumick B S. Maintainance of data cubes and summary tables in a warehouse. InProc. ACM-SIGMOD Int. Conf. Management of Data, Tucson, Arizona, 1997, pp.100–111.Google Scholar
  17. [17]
    Ross K A, Srivastava D, Sudarshan S. Materialized view maintenance and integrity constraint checking: Trading space for time. InProc. ACM-SIGMOD Int. Conf. Management of Data, Montreal, Canada, 1996, pp.447–458.Google Scholar

Copyright information

© Science Press, Beijing China and Allerton Press Inc. 2004

Authors and Affiliations

  1. 1.Institute of Computing TechnologyThe Chinese Academy of SciencesBeijingP.R. China
  2. 2.School of ComputingNational University of SingaporeSingapore
  3. 3.Information SchoolRenmin University of ChinaBeijingP.R. China

Personalised recommendations