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Semantic-Based Process Mining: A Conceptual Model Analysis and Framework

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Innovations in Bio-Inspired Computing and Applications (IBICA 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1180))

Abstract

Semantics has been a major challenge when applying the Process Mining (PM) technique to real-time business processes. In theory, efforts to bridge the semantic gap has spanned the advanced notion of Semantic-based Process Mining (SPM). The SPM devotes its methods to the idea of making use of existing semantic technologies to support the analysis of PM techniques. Technically, the semantic-based process mining is applied through acquisition and representation of abstract knowledge about the domain processes in question. To this effect, this paper demonstrates how semantically focused process modelling and reasoning methods are used to improve the outcomes of PM techniques from the syntactic to a more conceptual level. Also, the work systematically reviews the current tools and methods that are used to support the outcomes of the process mining, and to this end, propose an SPM-based framework that proves to be more intelligent with a higher level of semantic reasoning aptitudes. In other words, this work provides a process mining approach that uses information (semantics) about different activities that can be found in any given process to generate rules and patterns through the method for annotation, conceptual assertions, and reasoning. Moreover, this is done to determine how the various activities that make up the said processes depend on each other or are performed in reality. In turn, the method is applied to enrich the informative values of the resultant models.

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Acknowledgment

The authors would like to acknowledge the technical and financial support of Writing Lab, TecLabs, Tecnologico de Monterrey, in the publication of this work.

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Authors and Affiliations

  1. Tecnologico de Monterrey, Writing Lab, TecLabs, Vicerrectoría de Investigación y Transferencia de Tecnología, 64849, Monterrey, NL, Mexico

    Kingsley Okoye

  2. School of Architecture Computing and Engineering, College of Arts Technologies and Innovation, University of East London, London, UK

    Kingsley Okoye

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  1. Kingsley Okoye
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Correspondence to Kingsley Okoye .

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR), Auburn, WA, USA

    Ajith Abraham

  2. Utkal University, Bhubaneswar, Odisha, India

    Mrutyunjaya Panda

  3. Department of Electronics Engineering, GIET University, Gunupur, Odisha, India

    Subhrajit Pradhan

  4. Area of Project Engineering, University of Cordoba, Córdoba, Córdoba, Spain

    Laura Garcia-Hernandez

  5. School of Information Science and Engineering, University of Jinan, Jinan, Shandong, China

    Kun Ma

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Okoye, K. (2021). Semantic-Based Process Mining: A Conceptual Model Analysis and Framework. In: Abraham, A., Panda, M., Pradhan, S., Garcia-Hernandez, L., Ma, K. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2019. Advances in Intelligent Systems and Computing, vol 1180. Springer, Cham. https://doi.org/10.1007/978-3-030-49339-4_20

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