Abstract
Reference models for data analysis with data warehouses may consist of multidimensional reference models and analysis graphs. Multidimensional reference models are best-practice domain-specific data models for online analytical processing. Analysis graphs are reference models of analysis processes for event-driven data analysis. Small and medium-sized enterprises (SMEs) as well as large multinational companies may benefit from the use of reference models for data analysis. The availability of multidimensional reference models lowers the obstacles that inhibit SMEs from using business intelligence (BI) technology. Multinational companies may define multidimensional reference models for increased compliance among subsidiaries and departments. Furthermore, the definition of analysis graphs facilitates the handling of business events for both SMEs and large companies. Modelers may customize the chosen reference models, tailoring the models to the specific needs of the individual company or local subsidiary. Customizations may consist of additions, omissions, and modifications with respect to the reference model. In this paper, we propose a metamodel and customization approach for multidimensional reference models and analysis graphs. We specifically address the explicit modeling of key performance indicators as well as the definition of analysis situations and analysis graphs.
Highlights
Reference models may describe both structural and behavioral aspects of data analysis
Evaluation: BIRD represents business ratios as calculated measures, each with a calculation rule that can be adapted to the data available in the data warehouse of a specific company
The association of a calculated measure with multiple fact classes in a multidimensional reference model may be regarded as a family of business ratios with the same calculation rule but with different base data, possibly at different granularities, that go into the calculation
Summary
Reference models may describe both structural and behavioral aspects of data analysis. The factors for nonproliferation of BI technology among SMEs include the nonexistence of a coherent definition of KPIs within the company, the mismatch between business needs and BI functionalities, the perceived complexity of handling and report building, and unqualified personnel as well as poorly structured data.[1] Other factors are a lack of BI-related know-how and the costs for implementation and deployment of a BI solution.[2] By providing a starting point for BI projects, reference models have the potential to reduce implementation and deployment costs for BI solutions and serve as a basis for requirements analysis together with business analysts.[3,4] Service providers may offer a set of preconfigured multidimensional reference models, including a catalog of KPIs, for different industries.
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