Abstract
Decision mining allows discovering rules that constraint the paths that the instances of a business process may follow during its execution. In Knowledge-intensive Processes (KiP), the discovery of such rules is a great challenge because they lack structure. In this context, this experimental study applies a decision mining technique in an event log of a real company that provides ICT infrastructure services. The log comprises structured data (ticket events) and non-structured data (messages exchanged among team members). The goal was to discover tacit decisions that could be potentially declared as business rules for the company. In addition to mining the decision points, we validated the discovered rule with the company w.r.t. their meaning.
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Campos, J., Richetti, P., Baião, F.A., Santoro, F.M. (2018). Discovering Business Rules in Knowledge-Intensive Processes Through Decision Mining: An Experimental Study. In: Teniente, E., Weidlich, M. (eds) Business Process Management Workshops. BPM 2017. Lecture Notes in Business Information Processing, vol 308. Springer, Cham. https://doi.org/10.1007/978-3-319-74030-0_44
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DOI: https://doi.org/10.1007/978-3-319-74030-0_44
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