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2009 International Conference on Advanced Information Networking and Applications Workshops
Workload Characterization of Autonomic DBMSs Using Statistical and Data Mining Techniques
Bradford, United Kingdom
May 26-May 29
ISBN: 978-0-7695-3639-2
ASCII Text
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Zerihun Zewdu, Mieso K. Denko, Mulugeta Libsie, "Workload Characterization of Autonomic DBMSs Using Statistical and Data Mining Techniques," Advanced Information Networking and Applications Workshops, International Conference on, pp. 244-249, 2009 International Conference on Advanced Information Networking and Applications Workshops, 2009.
 
 
BibTex
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@article{ 10.1109/WAINA.2009.159,
author = {Zerihun Zewdu and Mieso K. Denko and Mulugeta Libsie},
title = {Workload Characterization of Autonomic DBMSs Using Statistical and Data Mining Techniques},
journal ={Advanced Information Networking and Applications Workshops, International Conference on},
volume = {0},
year = {2009},
isbn = {978-0-7695-3639-2},
pages = {244-249},
doi = {http://doi.ieeecomputersociety.org/10.1109/WAINA.2009.159},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Advanced Information Networking and Applications Workshops, International Conference on
TI - Workload Characterization of Autonomic DBMSs Using Statistical and Data Mining Techniques
SN - 978-0-7695-3639-2
SP244
EP249
A1 - Zerihun Zewdu,
A1 - Mieso K. Denko,
A1 - Mulugeta Libsie,
PY - 2009
KW - autonomic databses
KW - autonomic computing
KW - data mining
KW - DBMS
KW - workload characterization
VL - 0
JA - Advanced Information Networking and Applications Workshops, International Conference on
ER -
 
In this paper a model where an autonomic DBMS can identify and characterize the type of workload acting upon it is developed and the most important database status variables which are highly affected by changing workloads are identified. Two algorithms are selected for database workload classification: hierarchical clustering and classification & regression tree for classifying database workloads after running database workloads from TPC (Transaction Processing Performance Council) benchmark queries and transactions. The costs of these workloads are measured in terms of status variables of MySQL. A set of extensive experiments and analyses have been conducted and the results are presented in this paper.
Index Terms:
autonomic databses, autonomic computing, data mining, DBMS, workload characterization
Citation:
Zerihun Zewdu, Mieso K. Denko, Mulugeta Libsie, "Workload Characterization of Autonomic DBMSs Using Statistical and Data Mining Techniques," waina, pp.244-249, 2009 International Conference on Advanced Information Networking and Applications Workshops, 2009
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