Investigating Inductive Miner and Fuzzy Miner in Automated Business Model Generation Michael Siek
Business Information Systems Program, Information Systems Department, Faculty of Computing and Media, Bina Nusantara University, Jakarta, Indonesia 11480
Abstract
In the rapid growth of the advanced technologies and digital business competitions, many enterprises must inevitably adopt the new technologies toward strong competitiveness in the changing market. In practice, the business transformation is not that easy to be implemented and this often modifies the current business processes dramatically. Along with these technology adoptions, there is an emerging research field so-called process mining that studies on the utilization of data science principles in business process generation, conformance checking, and bottleneck identification. This paper focuses on investigating the modelling results of inductive miner and fuzzy miner algorithms for automated business model generation from event log data. In relation to data-driven method, the business model generation using process mining could provide a soft adaptation of the new business process implementation in the enterprises through incremental and continuous improvements.
Keywords: business process model, Petri net, data mining, event log data, induction algorithms
