Query Recommendations for OLAP Discovery-Driven Analysis

Arnaud Giacometti (1) , Patrick Marcel (1) , Elsa Negre (2) , Arnaud Soulet (1)

1 BDTLN - Bases de données et traitement des langues naturelles
2 LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision

Recommending database queries is an emerging and promising field of investigation. This is of particular interest in the domain of OLAP systems where the user is left with the tedious process of navigating large datacubes. In this paper we present a framework for a recommender system for OLAP users, that leverages former users' investigations to enhance discovery driven analysis. The main idea is to recommend to the user the discoveries detected in those former sessions that investigated the same unexpected data as the current session.

Mots clés: OLAP analysis; MDX queries; recommendation

Domaines: Base de données [cs.DB]

Arnaud Giacometti, Patrick Marcel, Elsa Negre, Arnaud Soulet. Query Recommendations for OLAP Discovery-Driven Analysis. International Journal of Data Warehousing and Mining (IJDWM), 2011, 7 (2), pp.1-25. ⟨10.4018/jdwm.2011040101⟩. ⟨hal-01021237⟩