Admire: An Algebraic Approach To System Performance Analysis Using Data Mining Techniques
Data Mining; Regression; Scalability
System performance analysis is a very difficult problem. Traditional tools rely on manual operations to analyze data. Consequently, determining which system resources to examine is often a lengthy process, where many problems are elusive, even when using data mining tools. We address this problem by introducing the Analyzer for Data Mining Results (ADMiRe) technique as a natural and flexible means to further interpret data mining outcome. In our scheme, regression analysis is first applied to performance data to discover correlations between parameters. Regression rules are defined to represent this output in a format suitable for ADMiRe. ADMiRe expressions are then used to manipulate these sets of rules, revealing information about combined, common and different features of varying configurations. This knowledge would be unavailable if regression output were considered in isolation. ADMiRe was tested with performance data collected from a TPC-C (Transaction Processing Performance Council) test on an Oracle database system, under various configurations, to demonstrate the effectiveness of our technique.
Proceedings of the ACM Symposium on Applied Computing
Number of Pages
Article; Proceedings Paper
Source API URL
Hua, Kien A.; Jiang, Ning; and Villafane, Roy, "Admire: An Algebraic Approach To System Performance Analysis Using Data Mining Techniques" (2003). Scopus Export 2000s. 1681.