Log-driven user-centric OLAP

While OLAP (On-Line Analytical Processing) is now a mature, efficient technology, very little attention has been paid to the effectiveness of the analysis and the user-friendliness of a technology often considered tedious of use. This work summarizes various contributions to developing user-centric OLAP, focusing on the use of former queries to enhance subsequent analyses. We first introduce a logical model to describe and manipulate queries and logs, including a language parametrized by binary relations over analytical sessions. In particular, these relations can be specialization relations or based on similarity measures tailored for OLAP queries and sessions. We then describe how logs can be mined for knowledge representing the user behavior. This knowledge includes simple preferences, navigational habits and discoveries made during former explorations. We show how it can be used in various query personalization or query recommendation approaches, that vary in terms of formulation effort, proactiveness, prescriptiveness and expressive power. Finally, we remark that the models and approaches introduced can be seen as a starting point for assessing the effectiveness of analytical sessions, and bridging the gap between OLAP and exploratory search.