skip to main content
10.1145/1097002.1097005acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
Article

A personalization framework for OLAP queries

Published:04 November 2005Publication History

ABSTRACT

OLAP users heavily rely on visualization of query answers for their interactive analysis of massive amounts of data. Very often, these answers cannot be visualized entirely and the user has to navigate through them to find relevant facts.In this paper, we propose a framework for personalizing OLAP queries. In this framework, the user is asked to give his (her) preferences and a visualization constraint, that can be for instance the limitations imposed by the device used to display the answer to a query. Given this, for each query, our method computes the part of the answer that respects both the user preferences and the visualization constraint. In addition, a personalized structure for the visualization is proposed.

References

  1. R. Agrawal and E. L. Wimmers. A framework for expressing and combining preferences. In W. Chen, J. F. Naughton, and P. A. Bernstein, editors, Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, May 16-18, 2000, Dallas, Texas, USA, pages 297--306. ACM, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. L. Bellatreche and K. Boukhalfa. An evolutionary approach to schema partitioning selection in a data warehouse environment. To appear in Proceeding of the International Conference on Data Warehousing and Knowledge Discovery (DAWAK'2005), pages 115--125, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. L. Bellatreche, A. Giacometti, D. Laurent, P. Marcel, and H. Mouloudi. A framework for combining rule-based and cost-based approaches to optimize OLAP queries. Numéro spécial, Entrepôts de Données et Analyse en ligne, RNTI, to be published., 2005.Google ScholarGoogle Scholar
  4. S. Chaudhuri. Index selection for databases: A hardness study and a principled heuristic solution. IEEE Transactions on Knowledge and Data Engineering, 16(11):1313--1323, November 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. S. Chaudhuri and U. Dayal. An overview of data warehousing and olap technology. Sigmod Record, 26(1):65--74, March 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. J. Chomicki. Preference formulas in relational queries. ACM Trans. Database Syst., 28(4):427--466, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. M. Corporation. OLEDB for OLAP. Available at http://www.microsoft.com/ data/oledb/olap, 1998.Google ScholarGoogle Scholar
  8. B. Ganter and R. Wille. Formal Concept Analysis: Mathematical Foundations. Springer-Verlag New York, Inc., Secaucus, NJ, USA, 1997. Translator-C. Franzke. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. M. R. Garey and D. S. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. Freeman, San Francisco, 1979. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. H. Gupta, V. Harinarayan, A. Rajaraman, and J. Ullman. Index selection for olap. Proceedings of the International Conference on Data Engineering (ICDE), pages 208--219, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. W. Kießling. Foundations of preferences in database systems. In Proceedings of 28th International Conference on Very Large Data Bases, pages 311--322. Morgan Kaufmann, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. G. Koutrika and Y. E. Ioannidis. Personalization of queries in database systems. In ICDE, pages 597--608. IEEE Computer Society, 2004. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. A. S. Maniatis, P. Vassiliadis, S. Skiadopoulos, and Y. Vassiliou. Advanced visualization for olap. In DOLAP '03: Proceedings of the 6th ACM international workshop on Data warehousing and OLAP, pages 9--16, New York, NY, USA, 2003. ACM Press. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. P. Marcel. Modeling and querying multidimensional databases: An overview. Networking and Information Systems Journal, 2(5-6):515--548, 1999.Google ScholarGoogle Scholar
  15. P. E. O'Neil and D. Quass. Improved query performance with variant indexes. In J. Peckham, editor, Proceedings of ACM SIGMOD International Conference on Management of Data, pages 38--49. ACM Press, 1997. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. M. T. Özsu and P. Valduriez. Principles of Distributed Database Systems : Second Edition. Prentice Hall, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. A personalization framework for OLAP queries

      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
        DOLAP '05: Proceedings of the 8th ACM international workshop on Data warehousing and OLAP
        November 2005
        122 pages
        ISBN:1595931627
        DOI:10.1145/1097002

        Copyright © 2005 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: 4 November 2005

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • Article

        Acceptance Rates

        Overall Acceptance Rate29of79submissions,37%

        Upcoming Conference

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader