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Dynamic Workload for Schema Evolution in Data Warehouses: A Performance Issue

Dynamic Workload for Schema Evolution in Data Warehouses: A Performance Issue

Fadila Bentayeb, Cécile Favre, Omar Boussaid
Copyright: © 2010 |Pages: 19
ISBN13: 9781605667485|ISBN10: 160566748X|EISBN13: 9781605667492
DOI: 10.4018/978-1-60566-748-5.ch002
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MLA

Bentayeb, Fadila, et al. "Dynamic Workload for Schema Evolution in Data Warehouses: A Performance Issue." Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications, edited by Tho Manh Nguyen, IGI Global, 2010, pp. 28-46. https://doi.org/10.4018/978-1-60566-748-5.ch002

APA

Bentayeb, F., Favre, C., & Boussaid, O. (2010). Dynamic Workload for Schema Evolution in Data Warehouses: A Performance Issue. In T. Nguyen (Ed.), Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications (pp. 28-46). IGI Global. https://doi.org/10.4018/978-1-60566-748-5.ch002

Chicago

Bentayeb, Fadila, Cécile Favre, and Omar Boussaid. "Dynamic Workload for Schema Evolution in Data Warehouses: A Performance Issue." In Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development: Innovative Methods and Applications, edited by Tho Manh Nguyen, 28-46. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-748-5.ch002

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Abstract

A data warehouse allows the integration of heterogeneous data sources for identified analysis purposes. The data warehouse schema is designed according to the available data sources and the users’ analysis requirements. In order to provide an answer to new individual analysis needs, the authors previously proposed, in recent work, a solution for on-line analysis personalization. They based their solution on a user-driven approach for data warehouse schema evolution which consists in creating new hierarchy levels in OLAP (on-line analytical processing) dimensions. One of the main objectives of OLAP, as the meaning of the acronym refers, is the performance during the analysis process. Since data warehouses contain a large volume of data, answering decision queries efficiently requires particular access methods. The main issue is to use redundant optimization structures such as views and indices. This implies to select an appropriate set of materialized views and indices, which minimizes total query response time, given a limited storage space. A judicious choice in this selection must be cost-driven and based on a workload which represents a set of users’ queries on the data warehouse. In this chapter, the authors address the issues related to the workload’s evolution and maintenance in data warehouse systems in response to new requirements modeling resulting from users’ personalized analysis needs. The main issue is to avoid the workload generation from scratch. Hence, they propose a workload management system which helps the administrator to maintain and adapt dynamically the workload according to changes arising on the data warehouse schema. To achieve this maintenance, the authors propose two types of workload updates: (1) maintaining existing queries consistent with respect to the new data warehouse schema and (2) creating new queries based on the new dimension hierarchy levels. Their system helps the administrator in adopting a pro-active behaviour in the management of the data warehouse performance. In order to validate their workload management system, the authors address the implementation issues of their proposed prototype. This latter has been developed within client/server architecture with a Web client interfaced with the Oracle 10g DataBase Management System.

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