A Data Mining Approach to Identify Key Factors in Dependability Experiments

Our paper presents a novel approach for identifying the key infrastructural factors determining the behavior of systems in the presence of faults by the application of intelligent data processing methods on data sets obtained from dependability benchmarking experiments. Our approach does not rely on a-priori assumptions or human intuition about the dominant aspects enabling this way the investigation of highly complex COTS-based systems. The proposed approach is demonstrated using a commercial data mining tool from IBM on the data obtained from experiments conducted using the DBench-OLTP dependability benchmark. Results obtained with the proposed technique identified important key factors impacting performance and dependability that could not have been revealed by the dependability benchmark measures.

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