Houssem Jerbi

Preference-Based Recommendations for OLAP Analysis

This paper presents a framework for integrating OLAP and recommendations. We focus on the anticipatory recommendation process that assists the user during his OLAP analysis by proposing to him the forthcoming analysis step. We present a context-aware preference model that matches decision-makers intuition, and we discuss a preference-based approach for generating personalized recommendations.

Applying Recommendation Technology in OLAP Systems

OLAP systems offering multidimensional and large information space cannot solely rely on standard navigation but need to apply recommendations to make the analysis process easy and to help users quickly find relevant data for decision-making. In this paper, we propose a recommendation methodology for assisting the user during his decision-support analysis. The system helps the user in querying multidimensional data and exposes him to the most interesting patterns, i.e. it provides to the user anticipatory as well as alternative decision-support data. We provide a preference-based approach to apply such methodology.

Management of context-aware preferences in multidimensional databases

Personalization of information systems brings new challenges to OLAP technology. A key characteristic of emerging OLAP database systems will be customizability of their behaviour taking into account users preferences as well as their context of analysis. In this paper we define a personalization framework for OLAP database systems based on user context-aware preferences. We consider a qualitative preference model which handles user preferences on the multidimensional schema. Context is modelled as a tree of multidimensional components of an OLAP analysis (fact, measures, dimensions, parameters). We define some OLAP operations that support personalization. User queries are dynamically enhanced with his preferences and are aware of the analysis context.

Recommandation de requêtes dans les bases de données multidimensionnelles annotées

This paper deals with personalization of annotated OLAP systems. Constellation data are extended with annotations and user preferences. Annotations reflect the decision-maker experience, whereas preferences enable users to focus on the most interesting data. User preferences allow annotated contextual recommendations that assist the decision-maker during hislher multidimensional navigations.