Process mining has become an active area of research and while there are numerous papers on approaches to process mining there are fewer detailing its application to real industrial scenarios and its applicability in these spaces. In this paper we introduce the approach to process mining used in a number of multinational enterprises and then reflect upon the issues that have been encountered during our ongoing work. In our opinion these issues are a clear example of the challenges that need to be addressed during business process discovery from heterogeneous data.
Abstract. This article presents a lightweight data representation model designed to support real time monitoring of business processes. The model is based on a shared vocabulary defined using open standard representations (RDF) allowing independence and extremely flexible interoperability between applications. The main benefit of this representation is that it is transparent to the data creation and analysis processes; furthermore it can be extended progressively when new information is available. Business Process data represented with this data model can be easily published on-line and shared between applications. After the definition of the data model, in this article, we demonstrate that with the use of this representation it is possible to retrieve and make use of domain specific information without any previous knowledge of the process. This model is a novel approach to real-time process data representation and paves the road to a complete new breed of applications for business process analysis.
Despite the benefits of investing in Big Data systems are largely recognised, their adoption have been slower than expected. Actually, organisations and companies cannot migrate their systems to new a technological infrastructure without a safe integration to their legacy systems and data. For these reasons, it is required to evolve Big Data technologies with mature functions for supporting portability, interoperability and reusability. This paper illustrates a practical use case exploiting the Model-driven capabilities of the TOREADOR platform as a way to fast track the uptake of business-driven Big Data models.
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