Systematic Change Management in Dimensional Data Warehousing

With the widespread and increasing use of data warehousing in industry, the design of effective data warehouses and their maintenance has become a focus of attention. Independently of this, the area of temporal databases has been an active area of research for well beyond a decade. This article identifies shortcomings of so-called star schemas, which are widely used in industrial warehousing, in their ability to handle change and subsequently studies the application of temporal techniques for solving these shortcomings. Star schemas represent a new approach to database design and have gained widespread popularity in data warehousing, but while they have many attractive properties, star schemas do not contend well with so-called slowly changing dimensions and with state-oriented data. We study the use of so-called temporal star schemas that may provide a solution to the identified problems while not fundamentally changing the database design approach. More specifically, we study the relative database size and query performance when using regular star schemas and their temporal counterparts for state-oriented data. We also offer some insight into the relative ease of understanding and querying databases with regular and temporal star schemas.