Affiliations: [a] Writing Lab, TecLabs, Vicerrectoría de Investigación y Transferencia de Tecnología, Tecnologico de Monterrey, Monterrey CP, Nuevo Leon, 64849, Mexico | [b] School of Architecture Computing and Engineering, College of Arts Technologies and Innovation, University of East London, E16 2RD, UK | E-mail: [email protected]
Correspondence:
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Corresponding author: Writing Lab, TecLabs, Vicerrectoría de Investigación y Transferencia de Tecnología, Tecnologico de Monterrey, Monterrey CP, Nuevo Leon, 64849, Mexico. E-mail: [email protected].
Abstract: Semantics has been a major challenge when applying the process mining (PM) technique to real-time business processes. The several theoretical and practical efforts to bridge the semantic gap has spanned the advanced notion of the semantic-based process mining (SPM). Fundamentally, the SPM devotes its methods to the idea of making use of existing (semantic) technologies to support the analysis of PM techniques. In principle, the semantic-based process mining method is applied through the acquisition and representation of abstract knowledge about the domain processes in question. To this effect, this paper demonstrates how the semantic concepts and process modelling (reasoning) methods are used to improve the outcomes of PM techniques from the syntactic to a more conceptual level. To do this, the study proposes an SPM-based framework that shows to be intelligent with a high level of semantic reasoning aptitudes. Technically, this paper introduces a process mining approach that uses information (semantics) about different activities that can be found in any given process to make inferences and generate rules or patterns through the method for annotation, semantic reasoning, and conceptual assertions. In turn, the method is theoretically applied to enrich the informative values of the resultant models. Also, the study conducts and systematically reviews the current tools and methods that are used to support the outcomes of the process mining as well as evaluates the results of the different methods to determine the levels of impact and its implications for process mining.
Keywords: Semantic modelling, process mining, annotation, events log, ontologies, semantic reasoning, process models
