Article

ADMiRe: an algebraic approach to system performance analysis using data mining techniques

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Duc TranAuthors Info & Claims

SAC '03: Proceedings of the 2003 ACM symposium on Applied computing

Pages 490 - 496

https://doi.org/10.1145/952532.952629

Published: 09 March 2003 Publication History

Abstract

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.

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Cited By

Songyun Duan Hui Zhang Jiang GXiaoqiao Meng (2010)Supporting System-wide Similarity Queries for networked system management2010 IEEE Network Operations and Management Symposium - NOMS 201010.1109/NOMS.2010.5488451(567-574)Online publication date: Apr-2010
https://doi.org/10.1109/NOMS.2010.5488451
Hui Zhang Haifeng Chen Guofei Jiang Xiaoqiao Meng Kenji Yoshihira (2008)Fast statistical relationship discovery in massive monitoring dataIEEE INFOCOM 2008 - IEEE Conference on Computer Communications Workshops10.1109/INFOCOM.2008.4544613(1-6)Online publication date: Apr-2008
https://doi.org/10.1109/INFOCOM.2008.4544613
Jiang NVillafane RHua KSawant APrabhakara K(2005)ADMiReIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2005.10317:7(888-901)Online publication date: 1-Jul-2005
https://dl.acm.org/doi/10.1109/TKDE.2005.103

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cover image ACM Conferences

SAC '03: Proceedings of the 2003 ACM symposium on Applied computing

March 2003

1268 pages

ISBN:1581136242

DOI:10.1145/952532

Conference Chair:
Gary B. Lamont
Air Force Institute of Technology
,
Program Chairs:
Hisham Haddad
Kennesaw State University
,
George A. Papadopoulos
University of Cyprus, Cyprus
,
Publications Chair:
Brajendra Panda
University of Arkansas

Copyright © 2003 ACM.

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Published: 09 March 2003

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Cited By

Songyun Duan Hui Zhang Jiang GXiaoqiao Meng (2010)Supporting System-wide Similarity Queries for networked system management2010 IEEE Network Operations and Management Symposium - NOMS 201010.1109/NOMS.2010.5488451(567-574)Online publication date: Apr-2010
https://doi.org/10.1109/NOMS.2010.5488451
Hui Zhang Haifeng Chen Guofei Jiang Xiaoqiao Meng Kenji Yoshihira (2008)Fast statistical relationship discovery in massive monitoring dataIEEE INFOCOM 2008 - IEEE Conference on Computer Communications Workshops10.1109/INFOCOM.2008.4544613(1-6)Online publication date: Apr-2008
https://doi.org/10.1109/INFOCOM.2008.4544613
Jiang NVillafane RHua KSawant APrabhakara K(2005)ADMiReIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2005.10317:7(888-901)Online publication date: 1-Jul-2005
https://dl.acm.org/doi/10.1109/TKDE.2005.103

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