A new approach to I/O performance evaluation: self-scaling I/O benchmarks, predicted I/O performance
SIGMETRICS '93 Proceedings of the 1993 ACM SIGMETRICS conference on Measurement and modeling of computer systems
On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Self-similarity in file systems
SIGMETRICS '98/PERFORMANCE '98 Proceedings of the 1998 ACM SIGMETRICS joint international conference on Measurement and modeling of computer systems
Self-similarity and heavy tails: structural modeling of network traffic
A practical guide to heavy tails
Entropy-based subspace clustering for mining numerical data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Maintaining knowledge about temporal intervals
Communications of the ACM
Fast-Start: quick fault recovery in oracle
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Clustering by pattern similarity in large data sets
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Set-Oriented Mining for Association Rules in Relational Databases
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
An Interval Classifier for Database Mining Applications
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
ADMiRe: An Algebraic Data Mining Approach to System Performance Analysis
IEEE Transactions on Knowledge and Data Engineering
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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.