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

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, we previously proposed, in recent work, a solution for on-line analysis personalization. We based our 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, we 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, we 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, we 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. Our system helps the administrator in adopting a pro-active behaviour in the management of the data warehouse performance. In order to validate our workload management system, we address the implementation issues of our proposed prototype. This latter has been developed within client/server architecture with a web client interfaced with the Oracle 10g DataBase Management System.