Mining the smallest association rule set for predictions

©2001 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.Mining transaction databases for association rules usually generates a large number of rules, most of which are unnecessary when used for subsequent prediction. In this paper we define a rule set for a given transaction database that is much smaller than the association rule set but makes the same predictions as the association rule set by the confidence priority. We call this subset the informative rule set. The informative rule set is not constrained to particular target items; and it is smaller than the non-redundant association rule set. We present an algorithm to directly generate the informative rule set, i.e., without generating all frequent itemsets first, and that accesses the database less often than other unconstrained direct methods. We show experimentally that the informative rule set is much smaller than both the association rule set and the non-redundant association rule set, and that it can be generated more efficiently

Failure prediction for high-performance computing systems

The failure rate in high-performance computing (HPC) systems continues to escalate as the number of components in these systems increases. This affects the scalability and the performance of parallel applications in large-scale HPC systems. Fault tolerance (FT) mechanisms help mitigating the impact of failures on parallel applications. However, utilizing such mechanisms requires additional overhead. Besides, the overuse of FT mechanisms results in unnecessarily large overhead in the parallel applications. Knowing when and where failures will occur can greatly reduce the excessive overhead. As such, failure prediction is critical in order to effectively utilize FT mechanisms. In addition, it also helps in system administration and management, as the predicted failure can be handled beforehand with limited impact to the running systems. This dissertation proposes new proficiency metrics for failure prediction based on failure impact in UPC environment that the existing proficiency metrics tire unable to reflect. Furthermore, an efficient log message clustering algorithm is proposed for system event log data preprocessing and analysis. Then, two novel association rule mining approaches are introduced and employed for HPC failure prediction. Finally, the performances of the existing and the proposed association rule mining methods are compared and analyzed