Title :
Auto-recognizing DBMS Workload Based on C5.0 Algorithm
Author :
Niu, Zhixian ; Zong, Lili ; Yan, Qingwei ; Zhao, Zhenxing
Author_Institution :
Coll. of Comput. & Software, Taiyuan Univ. of Technol., Taiyuan
Abstract :
The type of the workload is one of the key factors on database management system (DBMS) tuning. Different types of workload (OLTP, online transaction processing and OLAP, online analytical processing) mean different resource allocation strategies. In this paper, we present an approach to automatically identify a DBMS workload as either OLTP or OLAP. We use C5.0 algorithm to construct a set of classifiers based on the characteristics that differentiate OLTP and OLAP and then use the classifier to identify the workload type. The experiments show that the classifiers can be able to accurately identify the OLTP and OLAP workloads.
Keywords :
data mining; database management systems; transaction processing; C5.0 algorithm; DBMS workload; database management system; online analytical processing; online transaction processing; resource allocation strategies; Classification tree analysis; Costs; Data mining; Database systems; Decision trees; Educational institutions; Resource management; Software algorithms; Testing; Transaction databases; C5.0 algorithm; OLAP; OLTP; classifier; self-managed DBMS;
Conference_Titel :
Knowledge Discovery and Data Mining, 2009. WKDD 2009. Second International Workshop on
Conference_Location :
Moscow
Print_ISBN :
978-0-7695-3543-2
DOI :
10.1109/WKDD.2009.185
