Two-Stage Stochastic View Selection for Data-Analysis Queries

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 186)

Abstract

We consider the problem of selecting an optimal set of views to answer a given collection of queries at the present time (stage 1) as well as several collections of queries in the future (stage 2), with a given probability of occurrence associated with each collection, so as to minimize the expected value of the corresponding query response time, while keeping the total size of the views within a given limit. We formulate this problem as a two-stage stochastic programming problem. We show that this model is equivalent to an integer programming (IP) model that can be solved via various commercial IP solvers. We also study the relationship between the queries and the views in this context and use this relationship to reduce the size of the corresponding IP model, hence increase the scalability of our proposed approach.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agrawal, S., Bruno, N., Chaudhuri, S., Narasayya, V.R.: AutoAdmin: Self-tuning database systems technology. IEEE Data Eng. Bull. 29(3), 7–15 (2006)Google Scholar
  2. 2.
    Agrawal, S., Chaudhuri, S., Narasayya, V.R.: Automated selection of materialized views and indexes in SQL databases. In: VLDB, pp. 496–505 (2000)Google Scholar
  3. 3.
    Asgharzadeh, Z.T.: Exact and inexact methods for solving the view and index selection problem for OLAP performance improvement. Phd dissertation, North Carolina State University (2010)Google Scholar
  4. 4.
    Asgharzadeh, Z.T., Chirkova, R., Fathi, Y.: Exact and inexact methods for solving the problem of view selection for aggregate queries. International Journal of Business Intelligence and Data Mining 4(3/4), 391–415 (2009)CrossRefGoogle Scholar
  5. 5.
    Asgharzadeh, Z.T., Chirkova, R., Fathi, Y., Stallmann, M.: Exact and inexact methods for selecting views and indexes for OLAP performance improvement. In: EDBT, pp. 311–322 (2008)Google Scholar
  6. 6.
    Birge, J.R., Louveaux, F.: Introduction to Stochastic Programming. Springer (1997)Google Scholar
  7. 7.
    Bruno, N., Chaudhuri, S.: Interactive physical design tuning. In: ICDE, pp. 1161–1164 (2010)Google Scholar
  8. 8.
    Bruno, N., Chaudhuri, S., Weikum, G.: Database tuning using online algorithms. In: Encyclopedia of Database Systems, pp. 741–744. Springer US (2009)Google Scholar
  9. 9.
    Chaudhuri, S., Dayal, U., Narasayya, V.R.: An overview of business intelligence technology. Communications of the ACM 54(8), 88–98 (2011)CrossRefGoogle Scholar
  10. 10.
    Chaudhuri, S., Narasayya, V.R., Weikum, G.: Database tuning using combinatorial search. In: Encyclopedia of Database Systems, pp. 738–741. Springer US (2009)Google Scholar
  11. 11.
    Chaudhuri, S., Weikum, G.: Self-management technology in databases. In: Encyclopedia of Database Systems, pp. 2550–2555. Springer US (2009)Google Scholar
  12. 12.
    Duan, S., Franklin, P., Thummala, V., Zhao, D., Babu, S.: Shaman: A self-healing database system. In: ICDE, pp. 1539–1542 (2009)Google Scholar
  13. 13.
    Harinarayan, V., Rajaraman, A., Ullman, J.D.: Implementing data cubes efficiently. In: SIGMOD, pp. 205–216 (1996)Google Scholar
  14. 14.
    Huang, R., Chirkova, R., Fathi, Y.: Two-stage stochastic view selection for data analysis. Tech. Rep. TR-2011-22, NC State University (2011), ftp://ftp.ncsu.edu/pub/unity/lockers/ftp/csc_anon/tech/2011/TR-2011-22.pdf
  15. 15.
    ILOG: CPLEX 11.0 software package (2007), http://www.ilog.com/products/cplex/
  16. 16.
    Kalnis, P., Mamoulis, N., Papadias, D.: View selection using randomized search. DKE 42, 89–111 (2002)MATHCrossRefGoogle Scholar
  17. 17.
    Kotidis, Y., Roussopoulos, N.: A case for dynamic view management. ACM TODS 26(4), 388–423 (2001)MATHCrossRefGoogle Scholar
  18. 18.
    Lightstone, S.: Physical database design for relational databases. In: Encyclopedia of Database Systems, pp. 2108–2114. Springer US (2009)Google Scholar
  19. 19.
    Shukla, A., Deshpande, P., Naughton, J.F.: Materialized view selection for multidimensional datasets. In: VLDB, pp. 488–499 (1998)Google Scholar
  20. 20.
    Theodoratos, D., Ligoudistianos, S., Sellis, T.K.: View selection for designing the global data warehouse. Data Knowledge and Engineering 39(3), 219–240 (2001)MATHCrossRefGoogle Scholar
  21. 21.
    Theodoratos, D., Sellis, T.K.: Incremental design of a data warehouse. Journal of Intelligent Information Systems 15(1), 7–27 (2000)CrossRefGoogle Scholar
  22. 22.
    TPC-H Revision 2.1.0: TPC Benchmark H (Decision Support), http://www.tpc.org/tpch/spec/tpch2.1.0.pdf
  23. 23.
    Wolsey, L.A.: Integer Programming. Wiley (1998)Google Scholar
  24. 24.
    Yang, J., Karlapalem, K., Li, Q.: Algorithms for materialized view design in data warehousing environment. In: VLDB, pp. 136–145 (1997)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Operations Research ProgramNC State UniversityRaleighUSA
  2. 2.Computer Science DepartmentNC State UniversityRaleighUSA

Personalised recommendations