Abstract
Process Aware Information Systems (PAIS) are IT systems which support business processes and generate event-logs as a result of execution of the supported business processes. Fuzzy-Miner (FM) is a popular algorithm within Process Mining which consists of discovering a process model from the event-logs. In traditional FM algorithm, the extracted process model consists of nodes and edges of equal value (in terms of the economic utility and objectives). However, in real-world applications, the actors, activities and transition between activities may not be of equal value. In this paper, we propose a Utility-Based Fuzzy Miner (UBFM) algorithm to efficiently mine a process model driven by a utility threshold. The term utility can be measured in terms of profit, value, quantity or other expressions of user’s preference. The focus of the work presented in this paper is to incorporate the statistical (based on frequency) and semantic (based on user’s objective) aspects while driving a process model. We conduct experiments on real-world dataset and synthetic dataset to demonstrate the effectiveness of our approach.
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Anand, K., Gupta, N., Sureka, A. (2015). Utility-Based Control Flow Discovery from Business Process Event Logs. In: Kumar, N., Bhatnagar, V. (eds) Big Data Analytics. BDA 2015. Lecture Notes in Computer Science(), vol 9498. Springer, Cham. https://doi.org/10.1007/978-3-319-27057-9_5
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DOI: https://doi.org/10.1007/978-3-319-27057-9_5
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