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An intelligent approach to data extraction and task identification for process mining

Author

Listed:
  • Jiexun Li

    (Oregon State University)

  • Harry Jiannan Wang

    (University of Delaware)

  • Xue Bai

    (University of Connecticut)

Abstract

Business process mining has received increasing attention in recent years due to its ability to provide process insights by analyzing event logs generated by various enterprise information systems. A key challenge in business process mining projects is extracting process related data from massive event log databases, which requires rich domain knowledge and advanced database skills and could be very labor-intensive and overwhelming. In this paper, we propose an intelligent approach to data extraction and task identification by leveraging relevant process documents. In particular, we analyze those process documents using text mining techniques and use the results to identify the most relevant database tables for process mining. The novelty of our approach is to formalize data extraction and task identification as a problem of extracting attributes as process components, and relations among process components, using sequence kernel techniques. Our approach can reduce the effort and increase the accuracy of data extraction and task identification for process mining. A business expense imbursement case is used to illustrate our approach.

Suggested Citation

  • Jiexun Li & Harry Jiannan Wang & Xue Bai, 2015. "An intelligent approach to data extraction and task identification for process mining," Information Systems Frontiers, Springer, vol. 17(6), pages 1195-1208, December.
    • Handle: RePEc:spr:infosf:v:17:y:2015:i:6:d:10.1007_s10796-015-9564-3
    DOI: 10.1007/s10796-015-9564-3
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    References listed on IDEAS

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    1. Aldowaisan, Tariq A. & Gaafar, Lotfi K., 1999. "Business process reengineering: an approach for process mapping," Omega, Elsevier, vol. 27(5), pages 515-524, October.
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    Cited by:

    1. Karl R. Lang & Vojislav B. Misic & Leon J. Zhao, 2015. "Special section on business process analytics," Information Systems Frontiers, Springer, vol. 17(6), pages 1191-1194, December.
    2. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2019. "Technology in the 21st century: New challenges and opportunities," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 321-335.
    3. Sheng, Jie & Amankwah-Amoah, Joseph & Wang, Xiaojun, 2017. "A multidisciplinary perspective of big data in management research," International Journal of Production Economics, Elsevier, vol. 191(C), pages 97-112.
    4. Victor Chang, 2020. "Presenting Cloud Business Performance for Manufacturing Organizations," Information Systems Frontiers, Springer, vol. 22(1), pages 59-75, February.
    5. Asef Pourmasoumi & Mohsen Kahani & Ebrahim Bagheri, 2017. "Mining variable fragments from process event logs," Information Systems Frontiers, Springer, vol. 19(6), pages 1423-1443, December.
    6. Junhyung Moon & Gyuyoung Park & Minyeol Yang & Jongpil Jeong, 2022. "Design and Verification of Process Discovery Based on NLP Approach and Visualization for Manufacturing Industry," Sustainability, MDPI, vol. 14(3), pages 1-27, January.
    7. Paul Rimba & An Binh Tran & Ingo Weber & Mark Staples & Alexander Ponomarev & Xiwei Xu, 2020. "Quantifying the Cost of Distrust: Comparing Blockchain and Cloud Services for Business Process Execution," Information Systems Frontiers, Springer, vol. 22(2), pages 489-507, April.
    8. Asef Pourmasoumi & Mohsen Kahani & Ebrahim Bagheri, 0. "Mining variable fragments from process event logs," Information Systems Frontiers, Springer, vol. 0, pages 1-21.

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