An Ontology-Based Autonomic System for Improving Data Warehouse Performances

Authors:
Vlad Nicolicin-Georgescu;Vincent Benatier;Remi Lehn;Henri Briand
Affiliations:
LINA CNRS 6241 - COD Team - Polytech'Nantes, Site Ecole Polytechnique de l'université de Nantes, Nantes, France 44306 and SP2 Solutions, La Roche sur Yon, France 85000;SP2 Solutions, La Roche sur Yon, France 85000;LINA CNRS 6241 - COD Team - Polytech'Nantes, Site Ecole Polytechnique de l'université de Nantes, Nantes, France 44306;LINA CNRS 6241 - COD Team - Polytech'Nantes, Site Ecole Polytechnique de l'université de Nantes, Nantes, France 44306
Venue:
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
Year:
2009

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Abstract

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.