Database Reference
In-Depth Information
Chapter 10
Data Warehouse Maintenance,
Evolution and Versioning
Johann Eder
University of Klagenfurt, Austria
Karl Wiggisser
University of Klagenfurt, Austria
ABSTRACT
Data Warehouses typically are building blocks of decision support systems in companies and public
administration. The data contained in a data warehouse is analyzed by means of OnLine Analytical
Processing tools, which provide sophisticated features for aggregating and comparing data. Decision
support applications depend on the reliability and accuracy of the contained data. Typically, a data
warehouse does not only comprise the current snapshot data but also historical data to enable, for in-
stance, analysis over several years. And, as we live in a changing world, one criterion for the reliability
and accuracy of the results of such long period queries is their comparability. Whereas data warehouse
systems are well prepared for changes in the transactional data, they are, surprisingly, not able to deal
with changes in the master data. Nonetheless, such changes do frequently occur. The crucial point for
supporting changes is, first of all, being aware of their existence. Second, once you know that a change
took place, it is important to know which change (i.e., knowing about differences between versions and
relations between the elements of different versions). For data warehouses this means that changes are
identified and represented, validity of data and structures are recorded and this knowledge is used for
computing correct results for OLAP queries. This chapter is intended to motivate the need for powerful
maintenance mechanisms for data warehouse cubes. It presents some basic terms and definitions for the
common understanding and introduces the different aspects of data warehouse maintenance. Furthermore,
several approaches addressing the problem are presented and classified by their capabilities.
Search WWH ::
Custom Search