Mokrane Bouzeghoub

Quality in Data Warehousing

Data warehousing is a new technology which provides a software infrastructure for decision support systems and OLAP applications. Data warehouse systems collect data from heterogeneous and distributed sources, transform and reconcile this data in order to aggregate it and customize it with respect to business and organizational criteria required by decision makers. High level aggregated data is organized by subjects and stored as a multidimensional structure into a data mart. Data quality is very important in database applications in general and very crucial in data warehousing in particular. Indeed, data warehouse systems provide aggregated data to decision makers whose actions and decisions should be very strategic to the enterprise. Providing dirty data, imprecise data or non coherent data may lead to the rejection of the decision support system or may result into non productive decisions. This chapter provides a general framework for data warehouse design based on quality.

Constraint confrontation: An important step in view integration

This paper addresses the problem of constraint integration in database design. The approach is inserted in an incremental database design methodology supported by the design environment KHEOPS. The view integration step using semantic unification is followed by the initial constraint confrontation. The detection phase is a deductive process in which the contradictions and redundancies between constraints are exhibited. The following conflict resolution depends on the strategy initially chosen by the designer. Four strategies are presented and discussed. An example of use is given to illustrate the application of those strategies. This approach could be enlarged in an object oriented context where schemata would have not only constraints but also methods to compare.