Abstract
Dynamically adapting business process to changing needs, and promptly reacting to events are today key factors to maintain competitiveness in the market. Business Process Management (BPM) is focusing today more and more on a BPM in the large approach to process changes that embrace all of the specific techniques and mechanisms needed to design, enact, execute and monitor processes and process-aware information systems. A novel and promising feature of BPM in the large is the ability to store, aggregate and combine this huge and very diverse amount of data that can enable new ways of analysing current operations and can deliver new business insights. The KITE.IT Project [2] is aimed at facing such challenges in the context of the Italian aerospace industry, using and integrating Open Source tools exclusively. The project has recently deployed its initial open framework offering a robust data integration system in an open and scalable architecture. In such a context a metamodel approach was considered the very first base to design a system apt at integrating data originated from heterogeneous sources. The clear advantage that can be reached is the improvement of the speed and the effectiveness of business operations. The clear advantage, which can be reached, is the improvement of the effectiveness of business operations. This paper, in particular, presents the design process that was implemented in defining the KITE.IT Metrics Metamodel (KMM). A final evaluation of the framework, as it was initially deployed, is also reported.
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Notes
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we have a relation for each arrow head in the figure.
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Thus, including Attractive, Performance, and Must be.
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This work was partly funded by the Italian Ministry of Economic Development under the Industria 2015 contract - KITE.IT project.
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Azzini, A. et al. (2015). Heterogeneous Business Process Management: A Metamodel-Based Approach. In: Uden, L., Heričko, M., Ting, IH. (eds) Knowledge Management in Organizations. KMO 2015. Lecture Notes in Business Information Processing, vol 224. Springer, Cham. https://doi.org/10.1007/978-3-319-21009-4_7
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