期刊名称：International Journal of Data Mining & Knowledge Management Process
印刷版ISSN：2231-007X
电子版ISSN：2230-9608
出版年度：2013
卷号：3
期号：6
DOI：10.5121/ijdkp.2013.3601
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Poor performance can turn a successful data warehousing project into a failure. Consequently, several attempts have been made by various researchers to deal with the problem of scheduling the Extract-Transform-Load (ETL) process. In this paper we therefore present several approaches in the context of enhancing the data warehousing Extract, Transform and loading stages. We focus on enhancing the performance of extract and transform phases by proposing two algorithms that reduce the time needed in each phase through employing the hidden semantic information in the data. Using the semantic information, a large volume of useless data can be pruned in early design stage. We also focus on the problem of scheduling the execution of the ETL activities, with the goal of minimizing ETL execution time. We explore and invest in this area by choosing three scheduling techniques for ETL. Finally, we experimentally show their behavior in terms of execution time in the sales domain to understand the impact of implementing any of them and choosing the one leading to maximum performance enhancement
