Authors:
Vlad Nicolicin-Georgescu
1
;
Vincent Benatier
1
;
Remi Lehn
2
and
Henri Briand
2
Affiliations:
1
SP2 Solutions, France
;
2
LINA CNRS 6241, COD Team, Ecole Polytechnique de l’Unviersité de Nantes, France
Keyword(s):
Autonomic Computing, Decision Support System, Data Warehouse, Ontology.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Biomedical Engineering
;
Cloud Computing
;
Data Engineering
;
Data Warehouses and OLAP
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Health Information Systems
;
Informatics in Control, Automation and Robotics
;
Information Systems Analysis and Specification
;
Intelligent Control Systems and Optimization
;
Knowledge Engineering
;
Knowledge Engineering and Ontology Development
;
Knowledge Management
;
Knowledge-Based Systems
;
Knowledge-Based Systems Applications
;
Ontologies and the Semantic Web
;
Ontology Engineering
;
Semantic Web Technologies
;
Services Science
;
Society, e-Business and e-Government
;
Software Agents and Internet Computing
;
Software Engineering
;
Symbolic Systems
;
Web Information Systems and Technologies
Abstract:
Complexity is the biggest challenge in managing information systems today, because of the continuous growth in data and information. As decision experts, we are faced with the problems generated by managing Decision Support Systems, one of which is the efficient allocation of shared resources. In this paper, we propose a solution for improving the allocation of shared resources between groups of data warehouses within a decision support system, with the Service Levels Agreements and Quality of Service as performance objectives. We base our proposal on the notions of autonomic computing, by challenging the traditional way of autonomic systems and by taking into consideration decision support systems’ special characteristics such as usage discontinuity or service level specifications. To this end, we propose the usage of specific heuristics for the autonomic self-improvement and integrate aspects of semantic web and ontology engineering as information source for knowledge base represen
tation, while providing a critical view over the advantages and disadvantages of such a solution.
(More)