An Ontology-Based Autonomic System for Improving Data Warehouse Performances

Vlad Nicolicin-Georgescu (1) , Vincent Benatier (2) , Rémi Lehn (1) , Henri Briand (1)

1 LINA - Laboratoire d'Informatique de Nantes Atlantique
2 SP2 Solutions

With the increase in the amount and complexity of information, data warehouse performance has become a constant issue, especially for decision support systems. As decisional experts are faced with the management of more complex data warehouses, a need for autonomic management capabilities is shown to help them in their work. Implementing autonomic managers over knowledge bases to manage them is a solution that we find more and more used in business intelligence environments. What we propose, as decisional system experts, is an autonomic system for analyzing and improving data warehouse cache memory allocations in a client environment. The system formalizes aspects of the knowledge involved in the process of decision making (from system hardware specifications to practices describing cache allocation) into the same knowledge base in the form of ontologies, analyzes the current performance level (such as query average response time values) and proposes new cache allocation values so that better performance is obtained.

Mots clés: decision support system; data warehouse; ontology; autonomic computing

Domaines: Autre [cs.OH]

Vlad Nicolicin-Georgescu, Vincent Benatier, Rémi Lehn, Henri Briand. An Ontology-Based Autonomic System for Improving Data Warehouse Performances. Knowledge-Based and Intelligent Information and Engineering Systems 13th International Conference, KES 2009, Sep 2009, Santiago, Chile. pp.262-269. ⟨hal-00422472⟩