Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

OLAP Personalization and Recommendation

  • Patrick Marcel
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_3191

Definition

Personalizing or recommending OLAP queries aims at making the OLAP user experience less disorientating when navigating huge amounts of multidimensional data (also called cubes). Such approaches allow coping with too many or too few query results, or suggesting new queries to pursue the navigation. Personalization allows adding preferences to a query for filtering out irrelevant results or ranking the results to focus on the most relevant first. It also allows turning selection predicates (hard constraints) into preferences (soft constraints) to favor nonempty answers. On the other end, recommendation allows to leverage the cube instance and/or past navigations on it to complement the current query result.

The general problem can be formally defined by given a sequence of queries S = <q 1, …, q c > (a session from now on) over an instance I of a cube schema C, a user profile P(consisting of ordered multidimensional objects), and a set of past sessions L (a log from now on),...
This is a preview of subscription content, log in to check access.

Recommended Readings

  1. 1.
    Aligon J, Similarity based recommendation of OLAP sessions, doctoral dissertation, Université François Rabelais Tours, France; 2013.Google Scholar
  2. 2.
    Aligon J, Golfarelli M, Marcel P, Rizzi S, Turricchia E. Mining preferences from OLAP query logs for proactive personalization. In: Proceedings of the 15th East European Conference on Advances in Databases and Information Systems; 2011. p. 84–97.CrossRefGoogle Scholar
  3. 3.
    Aligon J, Golfarelli M, Marcel P, Rizzi S, Turricchia E. Similarity measures for OLAP sessions. Knowl Inf Syst. 2014;39(2):463–89.CrossRefGoogle Scholar
  4. 4.
    Aufaure M-A, Beauger NK, Marcel P, Rizzi S, Vanrompay Y. Predicting your next OLAP query based on recent analytical sessions. In: Proceedings of the 15th International Conference on Data Warehousing and Knowledge Discovery; 2013. p. 134–45.CrossRefGoogle Scholar
  5. 5.
    Bellatreche L, Giacometti A, Marcel P, Mouloudi H, Laurent D. A personalization framework for OLAP queries. In: Proceedings of the ACM 8th International Workshop on Data Warehousing and OLAP; 2005. p. 9–18.Google Scholar
  6. 6.
    Giacometti A, Marcel P, Negre E. A framework for recommending OLAP queries. In: Proceedings of the ACM 11th International Workshop on Data Warehousing and OLAP; 2008. p. 73–80.Google Scholar
  7. 7.
    Giacometti A, Marcel P, Negre E. Recommending multidimensional queries. In: Proceedings of the 10th International Conference on Data Warehousing and Knowledge Discovery; 2009. p. 453–66.CrossRefGoogle Scholar
  8. 8.
    Giacometti A, Marcel P, Negre E, Soulet A. Query recommendations for OLAP discovery-driven analysis. Int J Data Warehouse Min. 2011;7(2):1–25.CrossRefGoogle Scholar
  9. 9.
    Golfarelli M, Rizzi S, Biondi P. myOLAP: an approach to express and evaluate OLAP preferences. IEEE Trans Knowl Data Eng. 2011;23(7):1050–64.CrossRefGoogle Scholar
  10. 10.
    Jerbi H, Ravat F, Teste O, Zurfluh G. Preference-based recommendations for OLAP analysis. In: Proceedings of the 10th International Conference on Data Warehousing and Knowledge Discovery; 2009. p. 467–78.CrossRefGoogle Scholar
  11. 11.
    Motro H. Cooperative database systems. In: Encyclopedia of library and information science. vol. 66 Supp 29. New York: Marcel Dekker; 2000. p. 79–97.
  12. 12.
    Sapia C. PROMISE: predicting query behavior to enable predictive caching strategies for OLAP systems. In: Proceedings of the 2nd International Conference on Data Warehousing and Knowledge Discovery; 2000. p. 224–33.CrossRefGoogle Scholar
  13. 13.
    Sarawagi S. iDiff: informative summarization of differences in multidimensional aggregates. Data Min Knowl Discov. 2001a;5(4):255–76.zbMATHCrossRefGoogle Scholar
  14. 14.
    Sarawagi S. User-cognizant multidimensional analysis. VLDB J. 2001b;10(2–3):224–39.zbMATHGoogle Scholar
  15. 15.
    Stefanidis K, Koutrika G, Pitoura E. A survey on representation, composition and application of preferences in database systems. ACM Trans Database Syst. 2011;36(3):19.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Département Informatique, Laboratoire d’InformatiqueUniversité François Rabelais ToursBloisFrance

Section editors and affiliations

  • Torben Bach Pedersen
    • 1
  • Stefano Rizzi
    • 2
  1. 1.Department of Computer ScienceAalborg UniversityAalborgDenmark
  2. 2.DISIUniversity of BolognaBolognaItaly