Advertisement

Optimal Query Mapping in Mobile OLAP

  • Ilias Michalarias
  • Hans-J. Lenz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4690)

Abstract

Query mapping to aggregation lattices is used in order to exploit sub-cube dependencies in multidimensional databases. It is employed in mobile OLAP dissemination systems, in order to reduce the number of handled data items and thus optimize their scheduling and dissemination process. This paper analyzes the impact of choosing between mapping to the data cube lattice or alternatively to the respective hierarchical data cube lattice. We analyze the involved tradeoffs and identify the exploitation degree of sub-cube derivability as the deciding factor. We therefore introduce an analytical framework which computes derivability related probabilities and thus facilitates the quantification of this degree. The information provided by the framework is consistent with experimental results of state of the art mobile OLAP dissemination systems.

Keywords

Hierarchical Level Lattice Node Mobile Client Data Cube Query Workload 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Sharaf, M., Chrysanthis, P.: On-demand data broadcasting for mobile decision making. Mobile Networks and Applications 9, 703–714 (2004)CrossRefGoogle Scholar
  2. 2.
    Huang, S.M., Lin, B., Deng, Q.S.: Intelligent cache management for mobile data warehouse systems. Journal of Database Management 16(2), 46–65 (2005)MathSciNetGoogle Scholar
  3. 3.
    Michalarias, I., Lenz, H.J.: Dissemination of multidimensional data using broadcast clusters. In: Chakraborty, G. (ed.) ICDCIT 2005. LNCS, vol. 3816, pp. 573–584. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Gray, J., Chaudhuri, S., Bosworth, A., Layman, A., Reichart, D., Venkatrao, M., Pellow, F., Pirahesh, H.: Data cube: A relational aggregation operator generalizing group-by, cross-tab, and sub totals. Data Min. Knowl. Discov. 1(1), 29–53 (1997)CrossRefGoogle Scholar
  5. 5.
    Gyssens, M., Lakshamanan, L.V.S.: Multidimensional data model and query language for infometrics. In: Proccedings of the 23rd. VLDB Conference, Athens, pp. 106–115 (1997)Google Scholar
  6. 6.
    Harinarayan, V., Rajaraman, A., Ullman, J.D.: Implementing data cubes efficiently. SIGMOD Rec. 25(2), 205–216 (1996)CrossRefGoogle Scholar
  7. 7.
    Kotidis, Y., Roussopoulos, N.: A case for dynamic view management. ACM Transactions on Database Systems 26(4), 388–423 (2001)zbMATHCrossRefGoogle Scholar
  8. 8.
    Baralis, E., Paraboschi, S., Teniente, E.: Materialized views selection in a multidimensional database. In: VLDB 1997. Proceedings of the 23rd International Conference on Very Large Data Bases, pp. 156–165. Morgan Kaufmann Publishers Inc., San Francisco (1997)Google Scholar
  9. 9.
    Shukla, A., Deshpande, P., Naughton, J.F., Ramasamy, K.: Storage estimation for multidimensional aggregates in the presence of hierarchies. In: Proceedings of the 22th International Conference on Very Large Data Bases, San Francisco, CA, USA, pp. 522–531. Morgan Kaufmann Publishers Inc., San Francisco (1996)Google Scholar
  10. 10.
    Lenz, H.J., Thalheim, B.: Olap databases and aggregation functions. In: SSDBM 2001. Proceedings of the Thirteenth International Conference on Scientific and Statistical Database Management, pp. 91–100. IEEE Computer Society Press, Washington (2001)Google Scholar
  11. 11.
    Vassiliadis, P., Skiadopoulos, S.: Modelling and optimisation issues for multidimensional databases. In: Wangler, B., Bergman, L.D. (eds.) CAiSE 2000. LNCS, vol. 1789, pp. 482–497. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  12. 12.
    Stanoi, I., Agrawal, D., Abbadi, A.E., Phatak, S.H., Badrinath, B.R.: Data warehousing alternatives for mobile environments. In: MobiDe 1999. Proceedings of the 1st ACM international workshop on Data engineering for wireless and mobile access, pp. 110–115. ACM Press, New York (1999)CrossRefGoogle Scholar
  13. 13.
    Cuzzocrea, A., Furfaro, F., Saccam, D.: Hand-olap: a system for delivering olap services on handheld devices. In: Proceedings of ISADS 2003, Pisa, Italy, pp. 213–224 (2003)Google Scholar
  14. 14.
    Maniatis, A., Vassiliadis, P., Skiadopoulos, S., Vassiliou, Y., Mavrogonatos, G., Michalarias, I.: A presentation model and non- traditional visualization for olap. International Journal of Data Warehousing & Mining 1(1), 1–36 (2005)Google Scholar
  15. 15.
    Sharaf, M.A., Chrysanthis, P.K.: Facilitating mobile decision making. In: WMC 2002, pp. 45–53. ACM Press, New York (2002)CrossRefGoogle Scholar
  16. 16.
    Sharaf, M.A., Sismanis, Y., Labrinidis, A., Chrysanthis, P., Roussopoulos, N.: Efficient dissemination of aggregate data over the wireless web. In: International Workshop on the Web and Databases(WebDB), pp. 93–98Google Scholar
  17. 17.
    Michalarias, I., Becker, C.: Multidimensional querying in wireless ad hoc networks. In: Proceedings of the 2007 ACM symposium on Applied computing, pp. 529–530. ACM Press, New York (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ilias Michalarias
    • 1
  • Hans-J. Lenz
    • 1
  1. 1.Freie Universität Berlin, Garystr. 21, 14195 BerlinGermany

Personalised recommendations