Conceptual Data Warehouse Design Methodology for Business Process Intelligence

Conceptual Data Warehouse Design Methodology for Business Process Intelligence

Svetlana Mansmann (University of Konstanz, Konstanz, Germany), Thomas Neumuth (Innovation Center Computer Assisted Surgery (ICCAS), Leipzig, Germany), Oliver Burgert (Innovation Center Computer Assisted Surgery (ICCAS), Leipzig, Germany) and Matthias Röger (University of Konstanz, Germany)
DOI: 10.4018/978-1-60566-748-5.ch007
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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.
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Business Process Intelligence

Business Process Intelligence (BPI) refers to the application of business intelligence techniques (including for example OLAP analysis and data mining) in business process management, with the goal of providing a better understanding of a company’s processes and of devising ways to improve them.” (Castellanos & Casati, 2005). Recent advances in the above techniques as well as in business process and business performance management have come together to enable a near real-time monitoring and measurement of business processes as to identify, interpret, and respond to critical business events.

According to Hall (2004), BPI can help companies improve their process management initiatives by:

  • providing a consistent, process-based view of the company,

  • facilitating real-time business process monitoring,

  • aligning execution with strategy,

  • managing enterprise performance.

The BPI approach overcomes the deficiencies of standard BPMS by storing process execution data in a data warehouse in a cleansed, transformed, and aggregated form (Dayal, et al., 2001). Such data can be analyzed using OLAP and data mining tools to support various knowledge extraction tasks that can be subdivided into the following subareas (Castellanos & Casati, 2005):

  • Process discovery is done by analyzing enterprise operations in order to derive the process model that can be used for automating process execution or increasing its efficiency.

  • Process mining and analysis seeks to identify interesting correlations helpful for forecasting, planning, or explaining certain phenomena.

  • Prediction is important for anticipating or preventing occurrence of certain situations.

  • Exception handling assists the analyst in addressing specific problems, for instance, by retrieving the data on how similar problems were handled in the past.

  • Static optimization is concerned with optimizing the process configuration against previously identified optimization areas.

  • Dynamic optimization is an intelligent component for supervising process instances at runtime in order to influence their execution as to maximize certain business objectives.

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