Abstract
Nowadays, it is common for an organization to maintain thousands of business processes. Technologies that provide automatic management for such amount of models are required. The objective of this paper is to deal with the problem of process model fragmentization, clustering and merging for the consolidation of Office Automation (OA) systems in China Mobile Communications Corporation (CMCC). After investigating the structural statistics of real-life process model samples, we propose an approach, based on the refined process structure tree (RPST) and software product line (SPL), to automatically identify reusable process fragments and merge similar ones into master fragments. These fragments can, for example, be used to facilitate the (re)design of numerous process models. Special attention is paid to the empirical study and statistics from the experiment on a sample set of 37 real-life OA processes. Lesson learned and problems to be further considered are also proposed.
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Notes
- 1.
Office Automation (OA) is one of the most important management information systems (MIS) for large enterprises in China, to process the information of daily office work for all staffs. It fundamentally refers to supporting enterprise general management business processes in offices, e.g., document flow, approval, transfer, and archive.
- 2.
The subsidiary organizations include 31 provincial companies, 1 design institute, and several specialized companies.
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- 5.
Maybe specific business behavior is divided into different pieces, or a fragment is a composite of some business behavior.
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Acknowledgments
This work is supported by the Ministry of Education & China Mobile Research Foundation (no.MCM20123011).
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Gao, X. et al. (2014). Process Model Fragmentization, Clustering and Merging: An Empirical Study. In: Lohmann, N., Song, M., Wohed, P. (eds) Business Process Management Workshops. BPM 2013. Lecture Notes in Business Information Processing, vol 171. Springer, Cham. https://doi.org/10.1007/978-3-319-06257-0_32
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DOI: https://doi.org/10.1007/978-3-319-06257-0_32
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