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A DMN-Based Method for Context-Aware Business Process Modeling Towards Process Variability

  • Rongjia SongEmail author
  • Jan Vanthienen
  • Weiping Cui
  • Ying Wang
  • Lei Huang
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 353)

Abstract

Business process modeling traditionally has not paid much attention to the interactive features considering the dynamism of the environment in which a business process is embedded. As context-awareness is accommodated in business process modeling, decisions are still considered within business processes in a traditional way. Moreover, context-aware business process modeling excessively relies on expert knowledge, due to a lack of a methodological way to guide its whole procedure. Lately, BPM (Business Process Management) is moving towards the separation of concerns paradigm by externalizing the decisions from the process flow. Most notably, the introduction of DMN (Decision Model and Notation) standard provides a solution and technique to model decisions and the process separately but consistently integrated. The DMN technique supports the ability to extract and operationalize value from data analytics since the value of data analytics lies in improving decision-making. In this paper, a DMN-based method is proposed for the separate consideration of decisions and business processes, which allows to model context into decisions as context-aware business process models for achieving business process variability. Using this method, the role of analytics in improving some part of the decision making can also be integrated in the context-aware business process modeling, which increases the potential for using big data and analytics to improve decision-making. Moreover, a formal presentation of DMN is extended with the context concept to set the theoretical foundation for the proposed DMN-based method.

Keywords

Decision modeling Context-aware business process Process modeling Process variability 

Notes

Acknowledgments

This research is supported by the Natural Science Foundation of China under Grant 71502010.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Rongjia Song
    • 1
    • 2
    Email author
  • Jan Vanthienen
    • 2
  • Weiping Cui
    • 3
  • Ying Wang
    • 1
  • Lei Huang
    • 1
  1. 1.Department of Information ManagementBeijing Jiaotong UniversityBeijingChina
  2. 2.Department of Decision Sciences and Information ManagementKU LeuvenLeuvenBelgium
  3. 3.State Grid Energy Research InstituteState Grid Corporation of ChinaBeijingChina

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