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The Application of a Semantic-Based Process Mining Framework on a Learning Process Domain

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Intelligent Systems and Applications (IntelliSys 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 868))

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Abstract

The process mining (PM) field combines techniques from computational intelligence which has been lately considered to encompass artificial intelligence (AI) or even the latter, augmented intelligence (AIs) systems, and the data mining (DM) to process modelling in order to analyze event logs. To this end, this paper presents a semantic-based process mining framework (SPMaAF) that exhibits high level of accuracy and conceptual reasoning capabilities particularly with its application in real world settings. The proposed framework proves useful towards the extraction, semantic preparation, and transformation of events log from any domain process into minable executable formats – with focus on supporting the further process of discovering, monitoring and improvement of the extracted processes through semantic-based analysis of the discovered models. Practically, the implementation of the proposed framework demonstrates the main contribution of this paper; as it presents a Semantic-Fuzzy mining approach that makes use of labels (i.e. concepts) within event logs about a domain process using a case study of the Learning Process. The paper provides a method which aims to allow for mining and improved analysis of the resulting process models through semantic – labelling (annotation), representation (ontology) and reasoning (reasoner). Consequently, the series of experimentations and semantically motivated algorithms shows that the proposed framework and its main application in real-world has the capacity of enhancing the PM results or outcomes from the syntactic to a much more abstraction levels.

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Author information

Authors and Affiliations

  1. School of Architecture Computing and Engineering, College of Arts, Technologies and Innovation, University of East London, Docklands Campus, 4-6 University Way, London, UK, E16 2RD

    Kingsley Okoye, Syed Islam, Usman Naeem, Mhd Saeed Sharif & Amin Karami

  2. Faculty of Telecom and Information Engineering, University of Engineering and Technology, Taxila, Pakistan

    Muhammad Awais Azam

Authors
  1. Kingsley Okoye
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  2. Syed Islam
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  3. Usman Naeem
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  4. Mhd Saeed Sharif
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  5. Muhammad Awais Azam
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  6. Amin Karami
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Corresponding author

Correspondence to Kingsley Okoye .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, UK

    Rahul Bhatia

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Okoye, K., Islam, S., Naeem, U., Sharif, M.S., Azam, M.A., Karami, A. (2019). The Application of a Semantic-Based Process Mining Framework on a Learning Process Domain. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 868. Springer, Cham. https://doi.org/10.1007/978-3-030-01054-6_96

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