A process mining methodology for modeling unstructured processes

Abstract

The effective representation of business processes is widely recognized as a critical task in Business Process Management (BPM). Unfortunately, the complexity of unstructured processes makes process modeling extremely challenging and limits the suitability of traditional model‐driven approaches, which appear considerably less effective and efficient. Nevertheless, most of the recent and promising data‐driven approaches dealing with unstructured processes are not yet completely developed and typically fail to provide an adequate procedural process model. This study proposes a novel process mining‐based methodology to achieve a significant process model when unstructured processes occur. Specifically, the method assesses and combines the outcomes of different process mining algorithms by evaluating the process model through appropriate quality parameters (i.e., accuracy and comprehensibility). The final output of the method corresponds to a unitary procedural process model that is mathematically computable, evaluable through objective quality metrics, comparable with other process models, convertible to other model languages, and usable for supporting BPM activities. Finally, a real case study of an Italian hospital is presented to verify the applicability of the proposed methodology.

Data-Driven Process Mining Framework for Risk Management in Construction Projects

“…Digital connectivity among designers, managers, workers, consumers, and physical industrial assets can unlock enormous value and provide significant benefits. PM can bring integrity into the application of data-driven approaches, most of which suffer from unstructured processes and fail to provide an adequate procedural model [10].…”

“…About the integration of AI and Process Mining, Ou-Yang and Winarjo (2011) [25] proposed a Petri Net integration approach to support multi-agent PM by using the alpha algorithm. Stefanini et al (2020) [10] proposed a PM-based methodology to achieve a procedural, comprehensive, and computable process model to deal with unstructured processes by assessing and combining the outcomes of different PM algorithms. Butt (2020) [26] offered an integrative business process management framework used as a reference model by academics and manufacturing organizations in their journey towards a successful transition from traditional manufacturing to Industry 4.0.…”

Online machine drawing pedagogy—A knowledge management perspective through maker education in theCOVID‐19 pandemic era

“…Knowledge management is a discipline, which is under a constant change, and its future depends on the development of automation mechanisms for knowledge flow, based on advanced cognitive capabilities, machine learning, and artificial intelligence (Nakash & Bouhnik, 2021). For unstructured and complex business and knowledge processes, methodologies like process mining can be adopted for analysis of the process (Stefanini et al, 2020). Emerging opportunities must be explored for better management of business as well as knowledge processes (Van Looy, 2021).…”

“…For unstructured and complex business and knowledge processes, methodologies like process mining can be adopted for analysis of the process (Stefanini et al, 2020). Emerging opportunities must be explored for better management of business as well as knowledge processes (Van Looy, 2021).…”

Analysis the patients’ careflows using process mining

“…The proposed framework has been applied on case studies in a hospital and a tour agency. Another application study was presented by [ 16 ] that proposed a new process mining method for simulating unstructured processes In order to achieve the most suitable process model, the proposed methodology permits the evaluation, comparison, and combination of the results of different process mining algorithms leveraging on conformance checking technique. The limitation of this study is related to the constraints of using Petri nets language for evaluating quality parameters, also another limitation is the possibility of lack of integration between coexisting information systems that cause lack of reliable event logs.…”