ONTOLOGY-BASED AUTONOMIC COMPUTING FOR RESOURCE SHARING BETWEEN DATA WAREHOUSES IN DECISION SUPPORT SYSTEMS

Vlad Nicolicin-Georgescu, Vincent Benatier, Remi Lehn, Henri Briand

2010

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 representation, while providing a critical view over the advantages and disadvantages of such a solution.

References

  1. Codd, E. F., Codd, S. & Salley, C. (1993), 'Providing olap to user-analysts: An it mandate'.
  2. Frolick, M. N. & Lindsey, K. (2003), 'Critical factors for data warehouse failure', Business Intelligence Journal 8(3), 48-54.
  3. Ganek, A. G. & Corbi, T. A. (2003), 'The dawning of the autonomic computing era', IBM Systems Journal 43(1), 5-18.
  4. Huebscher, M. & McCann, J. (2008), 'A survey on autonomic computing - degrees, models and applications', ACM Computing Surveys 40(3), 1-28.
  5. IBM (2001), An architectural blueprint for autonomic computing, IBM Corporation.
  6. Inmon, W. H. (1995), Tech topic: what is a data warehouse?, Prism solutions, Volume 1.
  7. Inmon, W. H. (2005), Building the data warehouse, fourth edition, Wiley Publishing.
  8. Lightstone, S. S., Lohman, G. & Zilio, D. (2002), 'Toward autonomic computing with db2 universal database', ACM SIGMOD Record 31(3), 55-61.
  9. Liu, L. & Özsu, M. T. (2008), Encyclopedia of Database Systems, Springer-Verlag. http://tomgruber.org/writing/ontology-definition2007.htm
  10. Maedche, A., Motik, B., Stojanovic, L., Studer, R. & Volz, R. (2003), 'Ontologies for enterprise knowledge management', IEEE Intelligent Systems 18(2), 26-33.
  11. Markl, V., Lohman, G. M. & Raman, V. (2003), 'Leo : An autonomic optimizer for db278, IBM Systems Journal 42(1), 98-106.
  12. Mateen, A., Raza, B. & Hussain, T. (2008), Autonomic computing in sql server, in 'Proceedings of the 7th IEEE/ACIS International Conference on Computer and Information Science, ICIS 200878, 113-118.
  13. Nicolicin-Georgescu, V., Benatier, V., Lehn, R. & Briand, H. (2009), An ontology-based autonomic system for improving data warehouse performances, in 'Knowledge-Based and Intelligent Information and Engineering Systems, 13th International Conference, KES200978, 261-268.
  14. Oracle (2010), 'Oracle Hyperion Essbase'. http://www.oracle.com/technology/products/bi/essbase /index.html
  15. Parshar, M. & Hariri, S. (2007), Autonomic Computing: Concepts, Infrastructure and Applications, CRC Press, Taylor & Francis Group.
  16. Saharia, A. N. & Babad, Y. M. (2000), 'Enhancing data warehouse performance through query caching', The DATA BASE Advances in Informatics Systems 31(2), 43-63.
  17. Sirin, E., Grau, B., Grau, B. C., Kalyanpur, A. & Katz, Y. (2007), 'Pellet: A practical owl-dl reasoner', Web Semantics: Science, Services and Agents on the World Wide Web 5(2), 51-53.
  18. Stanford Center for Biomedical Informatics Research (2010), http://protege.stanford.edu/
  19. Stojanovic, L., Schneider, J. M., Maedche, A. D., Libischer, S., Studer, R., Lumpp, T., Abecker, A., Breiter, G. & Dinger, J. (2004), 'The role of ontologies in autonomic computing systems', IBM Systems Journal 43(3), 598-616.
  20. Vassev, E. & Hinchey, M. (2009), 'Assl: A software engineering approach to autonomic computing', Computer 42(6), 90-93.
  21. Vassiliadis, P., Bouzeghoub, M. & Quix, C. (1999), Towards quality-oriented data warehouse usage and evolution, in 'Proceedings of the 11th International Conference on Advanced Information Systems Engineering, CAISE 9978, 164-179.
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Paper Citation


in Harvard Style

Nicolicin-Georgescu V., Benatier V., Lehn R. and Briand H. (2010). ONTOLOGY-BASED AUTONOMIC COMPUTING FOR RESOURCE SHARING BETWEEN DATA WAREHOUSES IN DECISION SUPPORT SYSTEMS . In Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 3: ICEIS, ISBN 978-989-8425-06-5, pages 199-206. DOI: 10.5220/0002895501990206


in Bibtex Style

@conference{iceis10,
author={Vlad Nicolicin-Georgescu and Vincent Benatier and Remi Lehn and Henri Briand},
title={ONTOLOGY-BASED AUTONOMIC COMPUTING FOR RESOURCE SHARING BETWEEN DATA WAREHOUSES IN DECISION SUPPORT SYSTEMS},
booktitle={Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 3: ICEIS,},
year={2010},
pages={199-206},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002895501990206},
isbn={978-989-8425-06-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 3: ICEIS,
TI - ONTOLOGY-BASED AUTONOMIC COMPUTING FOR RESOURCE SHARING BETWEEN DATA WAREHOUSES IN DECISION SUPPORT SYSTEMS
SN - 978-989-8425-06-5
AU - Nicolicin-Georgescu V.
AU - Benatier V.
AU - Lehn R.
AU - Briand H.
PY - 2010
SP - 199
EP - 206
DO - 10.5220/0002895501990206