Predicting your next OLAP query based on recent analytical sessions

Marie-Aude Aufaure (1) , Nicolas Kuchmann Beauger (1) , Patrick Marcel (2) , Stefano Rizzi (3) , Yves Vanrompay (1)

1 MAS - Mathématiques Appliquées aux Systèmes - EA 4037
2 LIFAT - Laboratoire d'Informatique Fondamentale et Appliquée de Tours
3 DISI - Department of Computer Science and Engineering [Bologna]

In Business Intelligence systems, users interact with data warehouses by formulating OLAP queries aimed at exploring multidimensional data cubes. Being able to predict the most likely next queries would provide a way to recommend interesting queries to users on the one hand, and could improve the efficiency of OLAP sessions on the other. In particular, query recommendation would proactively guide users in data exploration and improve the quality of their interactive experience. In this paper, we propose a framework to predict the most likely next query and recommend this to the user. Our framework relies on a probabilistic user behavior model built by analyzing previous OLAP sessions and exploiting a query similarity metric. To gain insight in the recommendation precision and on what parameters it depends, we evaluate our approach using different quality assessments.

Domaines: Intelligence artificielle [cs.AI]; Base de données [cs.DB]

Marie-Aude Aufaure, Nicolas Kuchmann Beauger, Patrick Marcel, Stefano Rizzi, Yves Vanrompay. Predicting your next OLAP query based on recent analytical sessions. 15th International conference on data warehousing and knowledge discovery (DaWaK 2013), Aug 2013, Prague, Czech Republic. 12 p., ⟨10.1007/978-3-642-40131-2_12⟩. ⟨hal-00831716⟩