Mining preferences from OLAP query logs for proactive personalization

Authors:
Julien Aligon;Matteo Golfarelli;Patrick Marcel;Stefano Rizzi;Elisa Turricchia
Affiliations:
Laboratoire d'Informatique, Université François Rabelais Tours, France;DEIS, University of Bologna, Italy;Laboratoire d'Informatique, Université François Rabelais Tours, France;DEIS, University of Bologna, Italy;DEIS, University of Bologna, Italy
Venue:
ADBIS'11 Proceedings of the 15th international conference on Advances in databases and information systems
Year:
2011

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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.