skip to main content
10.1145/288627.288645acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
Article
Free Access

Data cube approximation and histograms via wavelets

Authors Info & Claims
Published:01 November 1998Publication History
First page image

References

  1. 1.A. Agarwal et al. On the computation of multidimensional aggregates. In Proceedings o/the 1996 International Conference on Very Large Databases, Mumbai, India, 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. 2.A. Aggarwal and J. S. Vitter. The input/output complexity of sorting and related problems. Communications of the A CM, 31(9), 1116-1127, lOSS. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. 3.D. Barbara et al. The New Jersey data reduction report. Bulletin of the Technical Committee on Data Engineering, 20(4), xo07.Google ScholarGoogle Scholar
  4. 4.C. B. Databases. http://www.census.gov/.Google ScholarGoogle Scholar
  5. 5.D.L. Donoho. Unconditional bases are optimal bases for data compression and statistical estimation. Technical report, Department of Statistics, Stanford University, 1992.Google ScholarGoogle Scholar
  6. 6.P. B. Gibbons and Y. Matias. New sampling-based summary statistics for improving approximate query answers. In Proceedings of the 1998 A CM SIGMOD international Conference on Management of Data, Seattle, Washington, June 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. 7.J. Gray et al. Data cube: A relational aggregation operator generalizing group-by, cross-tabs and subtotals, in Proceedings of the 12th Annual IEEE Conference on Data Engineering (ICDE '96), 131-139, 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. 8.P. Haas and A. Swami. Sequential sampling procedures for query size estimation. In Proceedings of the 1992 A CM SIGMOD International Conference on Management of Data, 1992. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. 9.P. Haas and A. Swami. Sampling-based selectivity for joins using augmented frequent value statistics. In Proceedings of the 1995 A CM SIGMOD International Conference on Management of Data, March 1995. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. 10.V. Harinarayan et al. Implementing data cubes efficiently, in Proceedings of the 1996 A CM SIGMOD International Conference on Management o.f Data, Montreal, May 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. 11.J. M. Hellerstein et al. Online aggregation. In Proceedings of the 1997 ACM SIGMOD international Conference on Management of Data, Tucson, Arizona, May 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. 12.C.-T. Ho et al. Range queries in OLAP data cubes. In Proceedings of the 1997 A CM $IGMOD International Conference on Management of Data, Tucson, Arizona, May 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. 13.B. Jawerth and W. Sweldens. An overview of wavelet based muItiresolution analyses. SIAM Rev., 36(3), 377-412, 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. 14.R. Lipton and J. Naughton. Query size estimation by adaptive sampling. J. of Comput. Sys. Sci., 51, 18-25, 1985. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. 15.R. Lipton, J. Naughton, and D. Schneider. Practical selectivity estimation through adaptive sampling. In Proceeding of the 1990 A CM SIGMOD International Conference on Management o/Data, 1-11, 1990. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. 16.Y. Matias, J. S. Vitter, and M. Wang. Wavelet-based histograms for selectivity estimation. In Proceedings of the 1998 A CM $IGMOD International Conference on Management of Data, Seattle, Washington, June 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. 17.M. Muralikrishna and D. J. DeWitt. Equi-depth histograms for estimating selectivity factors for multi-dimensional queries. In Proceedings of the 1988 A CM SIGMOD International Conference on Management of Data, 28-36, 1988. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. 18.V. Poosala and Y. E. Ioannidis. Selectivity estimation without the attribute value independence assumption. In Proceedings of the 1997 International Conference on Very Large Databases, Athens, Greece, August 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. 19.S. Sarawagi and M. Stonebraker. Efficient organization of large multidimensional arrays. In Proceedings of the 11th Annual IEEE Conference on Data Engineering (ICDE '9d), Houston, Texas, 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. 20.J. E. Savage and J. S. Vitter. Parallelism in space-time tradeoffs. In F. P. Preparata, editor, Advances in Computing Research, Volume ~, 117-146. JAI Press, 1987.Google ScholarGoogle Scholar
  21. 21.E. J. Stollnitz, T. D. Derose, and D. H. Salesin. Wavelets .for Computer Graphics. Morgan Kaufmann, 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. 22.TPC benchmark D (decision support), 1995.Google ScholarGoogle Scholar
  23. 23.D. E. Vengroff and J. S. Vitter. I/O-efficient scientific computation using TPIE. In Proceedings of the Goddard Conference on Mass Storage Systems and Technologies, 553-570, College Park, MD, September 1996. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. 24.J. S. Vitter. External memory algorithms. In Proceedings o/the 1998 A CM Symposium on Principles of Database Systems, June 1998. Invited tutorial. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. 25.J. S. Vitter and E. A. M. Shriver. Algorithms for parallel memory I: Two-level memories. Algorithmica, 12(2-3), 110- 147, 1994. Special double issue on Large-Scale Memories.Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. 26.Y. Zhao, P. M. Deshpande, and J. F. Naughton. An arraybased algorithm for simultaneous multidimensional aggregates. In Proceedings of the 1997 A CM $IGMOD International Conference on Management o/Data, Tucson, Arizona, May 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Data cube approximation and histograms via wavelets

            Recommendations

            Comments

            Login options

            Check if you have access through your login credentials or your institution to get full access on this article.

            Sign in
            • Published in

              cover image ACM Conferences
              CIKM '98: Proceedings of the seventh international conference on Information and knowledge management
              November 1998
              450 pages
              ISBN:1581130619
              DOI:10.1145/288627

              Copyright © 1998 ACM

              Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

              Publisher

              Association for Computing Machinery

              New York, NY, United States

              Publication History

              • Published: 1 November 1998

              Permissions

              Request permissions about this article.

              Request Permissions

              Check for updates

              Qualifiers

              • Article

              Acceptance Rates

              Overall Acceptance Rate1,861of8,427submissions,22%

              Upcoming Conference

            PDF Format

            View or Download as a PDF file.

            PDF

            eReader

            View online with eReader.

            eReader