Abstract
The goal of personalization is to deliver information that is relevant to an individual or a group of individuals in the most appropriate format and layout. In the OLAP context personalization is quite beneficial, because queries can be very complex and they may return huge amounts of data. Aimed at making the user’s experience with OLAP as plain as possible, in this paper we propose a proactive approach that couples an MDX-based language for expressing OLAP preferences to a mining technique for automatically deriving preferences. First, the log of past MDX queries issued by that user is mined to extract a set of association rules that relate sets of frequent query fragments; then, given a specific query, a subset of pertinent and effective rules is selected; finally, the selected rules are translated into a preference that is used to annotate the user’s query. A set of experimental results proves the effectiveness and efficiency of our approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
Bellatreche, L., Giacometti, A., Marcel, P., Mouloudi, H., Laurent, D.: A personalization framework for OLAP queries. In: Proc. DOLAP, Bremen, Germany, pp. 9–18 (2005)
Golfarelli, M., Rizzi, S., Biondi, P.: myOLAP: An approach to express and evaluate OLAP preferences. In: IEEE TKDE (to appear 2011)
Jerbi, H., Ravat, F., Teste, O., Zurfluh, G.: Management of context-aware preferences in multidimensional databases. In: Proc. ICDIM, London, UK, pp. 669–675 (2008)
Jerbi, H., Ravat, F., Teste, O., Zurfluh, G.: Applying recommendation technology in OLAP systems. In: Filipe, J., Cordeiro, J. (eds.) ICEIS. LNBIP, vol. 24, pp. 220–233. Springer, Heidelberg (2009)
Biondi, P., Golfarelli, M., Rizzi, S.: Preference-based datacube analysis with myOLAP. In: Proc. ICDE (to appear 2011)
Stefanidis, K., Drosou, M., Pitoura, E.: ”You May Also Like” results in relational databases. In: Proc. PersDB, Lyon, France (2009)
Giacometti, A., Marcel, P., Negre, E., Soulet, A.: Query recommendations for OLAP discovery driven analysis. In: IJDWM (to appear 2011)
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)
Akbarnejad, J., Chatzopoulou, G., Eirinaki, M., Koshy, S., Mittal, S., On, D., Polyzotis, N., Varman, J.S.V.: SQL QueRIE recommendations. PVLDB 3(2), 1597–1600 (2010)
Khoussainova, N., Kwon, Y., Balazinska, M., Suciu, D.: Snipsuggest: Context-aware autocompletion for SQL. PVLDB 4(1), 22–33 (2010)
Veloso, A., de Almeida, H.M., Gonçalves, M.A., Meira Jr, W.: Learning to rank at query-time using association rules. In: Proc. SIGIR, Singapore, pp. 267–274 (2008).
Holland, S., Ester, M., Kießling, W.: Preference mining: A novel approach on mining user preferences for personalized applications. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 204–216. Springer, Heidelberg (2003)
Mobasher, B.: Data mining for web personalization. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web, pp. 90–135. Springer, Heidelberg (2007)
Microsoft: MDX reference (2009), http://msdn.microsoft.com/
Sá, C., Soares, C., Jorge, A., Azevedo, P., Costa, J.: Mining association rules for label ranking. In: Huang, J.Z., Cao, L., Srivastava, J. (eds.) PAKDD 2011, Part II. LNCS, vol. 6635, pp. 432–443. Springer, Heidelberg (2011)
Li, W., Han, J., Pei, J.: CMAR: Accurate and efficient classification based on multiple class-association rules. In: Proc. ICDM, pp. 369–376 (2001)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proc. VLDB, pp. 487–499. Santiago de Chile, Chile (1994)
Sarawagi, S.: Explaining differences in multidimensional aggregates. In: Proc. VLDB, Edinburgh, Scotland, pp. 42–53 (1999)
Sarawagi, S.: I3: Intelligent, interactive inspection of cubes (2009), http://www.cse.iitb.ac.in/sunita/icube/
Minnesota Population Center: Integrated public use microdata series (2008), http://www.ipums.org
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Aligon, J., Golfarelli, M., Marcel, P., Rizzi, S., Turricchia, E. (2011). Mining Preferences from OLAP Query Logs for Proactive Personalization. In: Eder, J., Bielikova, M., Tjoa, A.M. (eds) Advances in Databases and Information Systems. ADBIS 2011. Lecture Notes in Computer Science, vol 6909. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23737-9_7
Download citation
DOI: https://doi.org/10.1007/978-3-642-23737-9_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23736-2
Online ISBN: 978-3-642-23737-9
eBook Packages: Computer ScienceComputer Science (R0)