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Integration of OLAP and data mining for analysis of results from dependability evaluation experiments

Integration of OLAP and data mining for analysis of results from dependability evaluation... This paper proposes the application of On-Line Analytical Processing (OLAP) and data mining approaches to analyse the large amount of raw data collected in fault injection campaigns and dependability benchmarking experiments. We use data warehousing technologies to store raw results from different experiments in a multidimensional structure where raw data can be analysed by means of OLAP tools. Moreover, we present an approach for identifying the key infrastructural factors determining the behaviour of computer systems in the presence of faults by the application of data mining methods on the data sets. Results obtained with the proposed techniques identified important factors impacting performance and dependability that could not have been revealed solely by the benchmark measures. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Knowledge Management Studies Inderscience Publishers

Integration of OLAP and data mining for analysis of results from dependability evaluation experiments

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
1743-8268
eISSN
1743-8276
DOI
10.1504/IJKMS.2008.019753
Publisher site
See Article on Publisher Site

Abstract

This paper proposes the application of On-Line Analytical Processing (OLAP) and data mining approaches to analyse the large amount of raw data collected in fault injection campaigns and dependability benchmarking experiments. We use data warehousing technologies to store raw results from different experiments in a multidimensional structure where raw data can be analysed by means of OLAP tools. Moreover, we present an approach for identifying the key infrastructural factors determining the behaviour of computer systems in the presence of faults by the application of data mining methods on the data sets. Results obtained with the proposed techniques identified important factors impacting performance and dependability that could not have been revealed solely by the benchmark measures.

Journal

International Journal of Knowledge Management StudiesInderscience Publishers

Published: Jan 1, 2008

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