Identification of context-workflow dependencies in knowledge-intensive business processes using log analysis

Authors

DOI:

https://doi.org/10.15587/2312-8372.2016.86220

Keywords:

knowledge-intensive business process, process mining, process control

Abstract

Multivariate knowledge-intensive business processes that change at runtime on the basis of knowledge are considered. For more effective implementation of the process performers correct the course of its action with the help of their personal knowledge and experience. The dependencies that reflect the link between the execution context of the process actions are used. To improve the process control efficiency it is necessary to formalize the context-sensitive knowledge of artists and include them in the process model. Context states, as well as the sequence of process actions are recorded in the log of information system. This presents an opportunity to highlight the links between the context and the process based on log analysis. The method of selection of context-procedural dependencies of knowledge-intensive business process is proposed. This process involves the identification of repetitive sequences of events that reflect the process execution, as well as state of context artifacts and the relationships between artifacts that lead to the implementation of these actions. The method improves the efficiency of process control of knowledge-intensive business processes by supplementing the process mode by identifying dependencies.

Author Biographies

Виктор Макарович Левыкин, Kharkiv National University of Radio Electronics, Nauka ave., 16, Kharkiv, Ukraine, 61166

Doctor of Technical Sciences, Professor

Department of Information Control Systems

Оксана Викторовна Чалая, Kharkiv National University of Radio Electronics, Nauka ave., 16, Kharkiv, Ukraine, 61166

Candidate of Economic Sciences, Associate Professor

Department of Information Control Systems

References

  1. Vom Brocke, J., Rosemann, M. (2015). Handbook on Business Process Management 1. Introduction, Methods, and Information Systems. Springer-Verlag Berlin Heidelberg, 709. doi:10.1007/978-3-642-45100-3
  2. Weske, M. (2012). Business Process Management: Concepts, Languages, Architectures. Springer-Verlag Berlin Heidelberg, 403. doi:10.1007/978-3-642-28616-2
  3. Van der Aalst, W. M. P. (2011). Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer Berlin Heidelberg, 352. doi:10.1007/978-3-642-19345-3
  4. Gronau, N. (2012). Modeling and Analyzing knowledge intensive business processes with KMDL: Comprehensive insights into theory and practice (English). Gito, 522.
  5. Van der Aalst, W. M. P. (2014). Process Mining in the Large: A Tutorial. Business Intelligence. Springer Science + Business Media, 33–76. doi:10.1007/978-3-319-05461-2_2
  6. Easterby-Smith, M., Lyles, M. A. (2011). Handbook of Organizational Learning and Knowledge Management. John Wiley & Sons, 711. doi:10.1002/9781119207245
  7. Nonaka, I., von Krogh, G. (2009). Perspective – Tacit Knowledge and Knowledge Conversion: Controversy and Advancement in Organizational Knowledge Creation Theory. Organization Science, 20 (3), 635–652. doi:10.1287/orsc.1080.0412
  8. Cohn, D., Hull, R. (2009, September). Business artifacts: A data-centric approach to modeling business operations and processes. Bulletin of the IEEE Computer Society Technical Committee on Data Engineering, 32 (3), 1–7.
  9. Bhattacharya, K., Caswell, N. S., Kumaran, S., Nigam, A., Wu, F. Y. (2007). Artifact-centered operational modeling: Lessons from customer engagements. IBM Systems Journal, 46 (4), 703–721. doi:10.1147/sj.464.0703
  10. Görg, C., Pohl, M., Qeli, E., Xu, K. (2007). Visual Representations. Human-Centered Visualization Environments. Springer Science + Business Media, 163–230. doi:10.1007/978-3-540-71949-6_4
  11. Günther, C. W., Verbeek, E. (2014). OpenXES. Developer Guide. Technische Universiteit Eindhoven University of Technology, 38.
  12. Kalynychenko, O., Chalyi, S., Bodyanskiy, Y., Golian, V., Golian, N. (2013, September). Implementation of search mechanism for implicit dependences in process mining. 2013 IEEE 7th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS). Institute of Electrical and Electronics Engineers (IEEE). Available: https://doi.org/10.1109/idaacs.2013.6662657

Published

2016-11-24

How to Cite

Левыкин, В. М., & Чалая, О. В. (2016). Identification of context-workflow dependencies in knowledge-intensive business processes using log analysis. Technology Audit and Production Reserves, 6(1(32), 43–49. https://doi.org/10.15587/2312-8372.2016.86220