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KBCBP: A Knowledge-Based Collaborative Business Process Model Supporting Dynamic Procuratorial Activities and Roles

  • Hanyu Wu
  • Tun LuEmail author
  • Xianpeng Wang
  • Peng Zhang
  • Peng Jiang
  • Chunlin Xu
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1042)

Abstract

In recent years, the focus of business process management has gradually shifted from quantitative assessment to quality assessment. Business processes are no longer limited to the explicit rules, and the creativity and flexibility of the process have become more and more attractive. The data-driven process model drills this creativity and flexibility by collecting the data characteristics of actual instances and has been fully developed over the past decade. The knowledge-intensive process makes use of explicit and implicit knowledge, which is fit for procuratorial scenario. This paper combines the idea of the data-driven process model and knowledge-intensive process to propose a knowledge-based collaborative business process model KBCBP supporting dynamic procuratorial activities and roles. The mechanism of the model is based on the procuratorial background.

Keywords

Business process management Knowledge-intensive process Procuratorial scenario Dynamic process model 

Notes

Acknowledgments

This work was supported by the National Key Research and Development Program of China under Grant No. 2018YFC0381402.

References

  1. 1.
    Marjanovic, O., Seethamraju, R.: Understanding knowledge-intensive, practice-oriented business processes. In: Proceedings of the 41st Annual Hawaii International Conference on System Sciences (HICSS 2008), p. 373. IEEE (2008)Google Scholar
  2. 2.
    Marjanovic, O., Skaf-Molli, H., Molli, P., et al.: Collaborative practice-oriented business processes Creating a new case for business process management and CSCW synergy. In: 2007 International Conference on Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2007), pp. 448–455. IEEE (2007)Google Scholar
  3. 3.
    Aureli, S., Giampaoli, D., Ciambotti, M., et al.: Key factors that improve knowledge-intensive business processes which lead to competitive advantage. Bus. Process Manag. J. 25(1), 126–143 (2019)CrossRefGoogle Scholar
  4. 4.
    Rychkova, I., Nurcan, S.: Towards adaptability and control for knowledge-intensive business processes: declarative configurable process specifications. In: 44th Hawaii International Conference on System Sciences, pp. 1–10. IEEE (2011)Google Scholar
  5. 5.
    van der Aalst, W.M.P., Weske, M., Grünbauer, D.: Case handling: a new paradigm for business process support. Data Knowl. Eng. 53(2), 129–162 (2005)CrossRefGoogle Scholar
  6. 6.
    Steinau, S., Marrella, A., Andrews, K., et al.: DALEC: a framework for the systematic evaluation of data-centric approaches to process management software. Softw. Syst. Model. 18, 1–38 (2019)CrossRefGoogle Scholar
  7. 7.
    Meyer, A., Pufahl, L., Fahland, D., Weske, M.: Modeling and enacting complex data dependencies in business processes. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 171–186. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-40176-3_14CrossRefGoogle Scholar
  8. 8.
    Hull, R., et al.: Business artifacts with guard-stage-milestone lifecycles: managing artifact interactions with conditions and events. In: 5th ACM International Conference on Distributed Event-based Systems (DEBS), pp. 51–62. ACM, New York (2011)Google Scholar
  9. 9.
    Xu, W., Su, J., Yan, Z., Yang, J., Zhang, L.: An artifact-centric approach to dynamic modification of workflow execution. In: Meersman, R., et al. (eds.) OTM 2011. LNCS, vol. 7044, pp. 256–273. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-25109-2_17CrossRefGoogle Scholar
  10. 10.
    Künzle, V.: Object-aware process management. Ph.D. Thesis, University of Ulm (2013)Google Scholar
  11. 11.
    Kang, G., Yang, L., Xu, W., et al.: Artefact-centric business process configuration. Int. J. High Perform. Comput. Netw. 9(1–2), 93–103 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Hanyu Wu
    • 1
    • 2
    • 3
  • Tun Lu
    • 1
    • 2
    • 3
    Email author
  • Xianpeng Wang
    • 1
    • 2
    • 3
  • Peng Zhang
    • 1
    • 2
    • 3
  • Peng Jiang
    • 4
  • Chunlin Xu
    • 4
  1. 1.School of Computer ScienceFudan UniversityShanghaiChina
  2. 2.Shanghai Key Laboratory of Data ScienceFudan UniversityShanghaiChina
  3. 3.Shanghai Institute of Intelligent Electronics and SystemsShanghaiChina
  4. 4.TongFang SaiWeiXun Information Technology Co., Ltd.ChengduChina

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