Conceptual Data Warehouse Design Methodology for Business Process Intelligence

Abstract

The emerging area of business process intelligence aims at enhancing the analysis power of business process management systems by employing performance-oriented technologies of data warehousing and mining. However, the differences in the assumptions and objectives of the underlying models, namely the business process model and the multidimensional data model, aggravate straightforward and meaningful convergence of the two concepts. The authors present an approach to designing a data warehousing for enabling the multidimensional analysis of business processes and their execution. The aims of such analysis are manifold, from quantitative and qualitative assessment to process discovery, pattern recognition and mining. The authors demonstrate that business processes and workflows represent a non-conventional application scenario for the data warehousing approach and that multiple challenges arise at various design stages. They describe deficiencies of the conventional OLAP technology with respect to business process modeling and formulate the requirements for an adequate multidimensional presentation of process descriptions. Modeling extensions proposed at the conceptual level are verified by implementing them in a relational OLAP system, accessible via state-of-the-art visual frontend tools. The authors demonstrate the benefits of the proposed modeling framework by presenting relevant analysis tasks from the domain of medical engineering and showing the type of the decision support provided by our solution.