Advertisement

Summarizing and Querying Logs of OLAP Queries

  • Julien AligonEmail author
  • Patrick Marcel
  • Elsa Negre
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 471)

Abstract

Leveraging query logs benefits the users analyzing large data warehouses with OLAP queries. But so far nothing exists to allow the user to have concise and usable representations of what is in the log. In this article, we present a framework for summarizing and querying OLAP query logs. The basic idea is that a query summarizes another query and that a log, which is a sequence of queries, summarizes another log. Our formal framework includes a language to declaratively specify a summary, and a language for querying and manipulating logs. We also propose a simple measure based on precision and recall, to assess the quality of summaries, and two strategies for automatically computing log summaries of good quality. Finally we show how some simple properties on the summaries can be used to query the log efficiently. The framework is implemented using the Mondrian open source OLAP engine. Its interest is illustrated with experiments on synthetic yet realistic MDX query logs.

Keywords

Quality Measure Unary Operator General Query Query Expression OLAP Query 
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. [Aligon et al., 2010]
    Aligon, J., Marcel, P., Negre, E.: A framework for summarizing a log of OLAP queries. In: IEEE ICMWI, Special Track on OLAP and Data Warehousing (2010)Google Scholar
  2. [Aligon et al., 2011]
    Aligon, J., Marcel, P., Negre, E.: Résumé et interrogation de logs de requêtes OLAP. In: Proc. 11ème Conférence Internationale Francophone sur l’Extraction et la Gestion des Connaissances EGC (2011)Google Scholar
  3. [Chatzopoulou et al., 2009]
    Chatzopoulou, G., Eirinaki, M., Polyzotis, N.: Query Recommendations for Interactive Database Exploration. In: Winslett, M. (ed.) SSDBM 2009. LNCS, vol. 5566, pp. 3–18. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. [Chaudhuri et al., 2003]
    Chaudhuri, S., Ganesan, P., Narasayya, V.R.: Primitives for Workload Summarization and Implications for SQL. In: VLDB, pp. 730–741 (2003)Google Scholar
  5. [Colas et al., 2010]
    Colas, S., Marcel, P., Negre, E.: Organisation de log de requêtes OLAP sous forme de site web. In: EDA 2010. RNTI, vol. B-6, pp. 81–95, Cépaduès, Toulouse (2010)Google Scholar
  6. [Garcia-Molina et al., 2008]
    Garcia-Molina, H., Ullman, J.D., Widom, J.: Database Systems: The Complete Book. Prentice Hall Press, Upper Saddle River (2008)Google Scholar
  7. [Giacometti et al., 2009]
    Giacometti, A., Marcel, P., Negre, E.: Recommending Multidimensional Queries. In: Pedersen, T.B., Mohania, M.K., Tjoa, A.M. (eds.) DaWaK 2009. LNCS, vol. 5691, pp. 453–466. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  8. [Giacometti et al., 2011]
    Giacometti, A., Marcel, P., Negre, E., Soulet, A.: Query Recommendations for OLAP Discovery-Driven Analysis. IJDWM 7(2), 1–25 (2011)Google Scholar
  9. [Golfarelli, 2003]
    Golfarelli, M.: Handling Large Workloads by Profiling and Clustering. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds.) DaWaK 2003. LNCS, vol. 2737, pp. 212–223. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  10. [Khoussainova et al., 2009]
    Khoussainova, N., Balazinska, M., Gatterbauer, W., Kwon, Y., Suciu, D.: A case for a collaborative query management system. In: CIDR (2009)Google Scholar
  11. [Khoussainova et al., 2011]
    Khoussainova, N., Kwon, Y., Liao, W.-T., Balazinska, M., Gatterbauer, W., Suciu, D.: Session-Based Browsing for More Effective Query Reuse. In: Bayard Cushing, J., French, J., Bowers, S. (eds.) SSDBM 2011. LNCS, vol. 6809, pp. 583–585. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. [Lakshmanan et al., 2002]
    Lakshmanan, L.V.S., Pei, J., Han, J.: Quotient cube: How to summarize the semantics of a data cube. In: VLDB, pp. 778–789. Morgan Kaufmann (2002)Google Scholar
  13. [Ndiaye et al., 2010]
    Ndiaye, M., Diop, C.T., Giacometti, A., Marcel, P., Soulet, A.: Cube Based Summaries of Large Association Rule Sets. In: Cao, L., Feng, Y., Zhong, J. (eds.) ADMA 2010, Part I. LNCS, vol. 6440, pp. 73–85. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  14. [Peng et al., 2007]
    Peng, W., Perng, C., Li, T., Wang, H.: Event summarization for system management. In: Berkhin, P., Caruana, R., Wu, X. (eds.) KDD, pp. 1028–1032. ACM (2007)Google Scholar
  15. [Pitarch et al., 2010]
    Pitarch, Y., Laurent, A., Poncelet, P.: Summarizing Multidimensional Data Streams: A Hierarchy-Graph-Based Approach. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds.) PAKDD 2010 Part II. LNCS, vol. 6119, pp. 335–342. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. [Saint-Paul et al., 2005]
    Saint-Paul, R., Raschia, G., Mouaddib, N.: General purpose database summarization. In: VLDB, pp. 733–744 (2005)Google Scholar
  17. [Sarawagi, 1999]
    Sarawagi, S.: Explaining differences in multidimensional aggregates. In: VLDB, pp. 42–53 (1999)Google Scholar
  18. [Sarawagi, 2000]
    Sarawagi, S.: User-adaptive exploration of multidimensional data. In: VLDB, pp. 307–316 (2000)Google Scholar
  19. [Stefanidis et al., 2009]
    Stefanidis, K., Drosou, M., Pitoura, E.: ”You May Also Like” Results in Relational Databases. In: PersDB (2009)Google Scholar
  20. [Zadrozny and Kacprzyk, 2007]
    Zadrozny, S., Kacprzyk, J.: Summarizing the contents of web server logs: A fuzzy linguistic approach. In: FUZZ-IEEE, pp. 1–6. IEEE (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Laboratoire d’InformatiqueUniversité François Rabelais ToursToursFrance
  2. 2.LAMSADEUniversité Paris-DauphineParisFrance

Personalised recommendations