Different modelling approaches have been proposed to automate the process of design and development of data warehouse. Some of them use the model driven architecture, however they are more focused on designing platform independent model (PIM) and platform specific model (PSM) and reserved unfortunately few efforts for requirements modelling and design computational independent model (CIM). This situation can be explained essentially by the presence of mature technologies and methods used for the representation of PIM and PSM models. Moreover, there are no standards to build CIM. Within this context, data warehousing projects fail due to not much attention paid on the requirement analysis phase. To overcome this problem, this study provides an approach to build a CIM architecture for modelling data warehouse requirements using an unified modelling language (UML) profile that extends the use cases and business process models, and proposes requirements specifications using SBVR structured English.
Summary
The Model‐Driven Architecture (MDA) is an approach that aligns modeling and automation for software development. By applying such an approach to data warehouse (DW) projects, we can minimize a great deal of time and cost. Furthermore, most of OnLine Analytical Processing (OLAP) platforms seem to be like black boxes that provide wizards only to business intelligence developers to create and manipulate OLAP objects without allowing their sustainability and migration from a platform to another. That is why many works in the literature have proposed using the MDA approach in DW projects. However, most of them have mainly focused on the generation of the DW relational model from the conceptual one, and they overlooked the OLAP model and the cube implementation.
To deal with this problem, we propose in this paper an MDA solution to automate the process of getting OLAP cube and its implementation through a set of metamodels and automatic transformations among them. In fact, the proposal generates the OLAP and DW relational models (PSMs) from the conceptual one, using also a PDM model that describes the target business intelligence platform. After that, the source code to create the cube is got from both PSM models. For this aim, we define a set of transformation rules implemented using the Atlas transformation language. Finally, a case study will be provided to validate our approach.
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