An MDA Approach and QVT Transformations for the Integrated Development of Goal-Oriented Data Warehouses and Data Marts

Abstract

To customize a data warehouse, many organizations develop concrete data marts focused on a particular department or business process. However, the integrated development of these data marts is an open problem for many organizations due to the technical and organizational challenges involved during the design of these repositories as a complete solution. In this article, the authors present a design approach that employs user requirements to build both corporate data warehouses and data marts in an integrated manner. The approach links information requirements to specific data marts elicited by using goal-oriented requirement engineering, which are automatically translated into the implementation of corresponding data repositories by means of model-driven engineering techniques. The authors provide two UML profiles that integrate the design of both data warehouses and data marts and a set of QVT transformations with which to automate this process. The advantage of this approach is that user requirements are captured from the early development stages of a data-warehousing project to automatically translate them into the entire data-warehousing platform, considering the different data marts. Finally, the authors provide screenshots of the CASE tools that support the approach, and a case study to show its benefits.

Using Ontologies for the Design of Data Warehouses

“…Whichever approach is selected, there is a step in the development process where both structural and semantics mapping are involved. Despite of the fact that works such as [30,31] deal with the integrated design of data marts and data warehouses, they are mainly oriented to the structural design of databases. With regard to the structural mappings, data marts models are summarized or customized versions of the data warehouse counterparts where aggregation hierarchies are pruned and dimensions are limited (acting as materialized views).…”

Business Intelligence Indicators

“…Regarding the modeling of OLAP indicators, several conceptual models have been proposed being based on decision-makers point of view (Pardillo, 2011;Giorgini, 2008), but these models are all based on multidimensional modeling.…”