Hybrid methodology for data warehouse conceptual design by UML schemas
Data warehouse conceptual design is based on the metaphor of the cube, which can be derived from either requirement-driven or data-driven methodologies. Each methodology has its own advantages. The first allows designers to obtain a conceptual schema very close to the user needs but it may be not supported by the effective data availability. On the contrary, the second ensures a perfect traceability and consistence with the data sourcesin fact, it guarantees the presence of data to be used in analytical processingbut does not preserve from missing business user needs. To face this issue, the necessity emerged in the last years to define hybrid methodologies for conceptual design. The objective of the paper is to use a hybrid methodology based on different multidimensional models in order to gather all advantages of each of them. The proposed methodology integrates the requirement-driven strategy with the data-driven one, in that order, possibly performing alterations of functional dependencies on UML multidimensional schemas reconciled with data sources. As case study, we illustrate how our methodology can be applied to the university environment. Furthermore, we evaluate quantitatively the benefits of this methodology by comparing it with some popular and conventional methodologies. In conclusion, we highlight how the hybrid methodology improves the conceptual schema quality. Finally, we outline our present work devoted to introduce automatic design techniques in the methodology on the basis of the logical programming.
data warehouse conceptual design requirement analysis i* framework uml multidimensional model data modelling