Advertisement

An Artifact-Driven Approach to Monitor Business Processes Through Real-World Objects

  • Giovanni Meroni
  • Claudio Di Ciccio
  • Jan Mendling
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10601)

Abstract

Nowadays, many business processes once intra-organizational are becoming inter-organizational. Thus, being able to monitor how such processes are performed, including portions carried out by service providers, is paramount. Yet, traditional process monitoring techniques present some shortcomings when dealing with inter-organizational processes. In particular, they require human operators to notify when business activities are performed, and to stop the process when it is not executed as expected. In this paper, we address these issues by proposing an artifact-driven monitoring service, capable of autonomously and continuously monitor inter-organizational processes. To do so, this service relies on the state of the artifacts (i.e., physical entities) participating to the process, represented using the E-GSM notation. A working prototype of this service is presented and validated using real-world processes and data from the logistics domain.

Keywords

Artifact-driven process monitoring Physical artifacts E-GSM Inter-organizational monitoring service Autonomous process monitoring 

Notes

Acknowledgments

This work has been partially funded by the Italian Project ITS Italy 2020 under the Technological National Clusters program.

References

  1. 1.
    Akyildiz, I., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless sensor networks: a survey. Comput. Netw. 38(4), 393–422 (2002)CrossRefGoogle Scholar
  2. 2.
    Atzori, L., Iera, A., Morabito, G.: The internet of things: a survey. Comput. Netw. 54(15), 2787–2805 (2010)CrossRefzbMATHGoogle Scholar
  3. 3.
    Baresi, L., Meroni, G., Plebani, P.: A GSM-based approach for monitoring cross-organization business processes using smart objects. In: Reichert, M., Reijers, H.A. (eds.) BPM 2015. LNBIP, vol. 256, pp. 389–400. Springer, Cham (2016). doi: 10.1007/978-3-319-42887-1_32 CrossRefGoogle Scholar
  4. 4.
    Baresi, L., Meroni, G., Plebani, P.: Using the guard-stage-milestone notation for monitoring BPMN-based processes. In: Schmidt, R., Guédria, W., Bider, I., Guerreiro, S. (eds.) BPMDS/EMMSAD-2016. LNBIP, vol. 248, pp. 18–33. Springer, Cham (2016). doi: 10.1007/978-3-319-39429-9_2 Google Scholar
  5. 5.
    Baumgraß, A., Botezatu, M., Di Ciccio, C., Dijkman, R., Grefen, P., Hewelt, M., Mendling, J., Meyer, A., Pourmirza, S., Völzer, H.: Towards a methodology for the engineering of event-driven process applications. In: Reichert, M., Reijers, H.A. (eds.) BPM 2015. LNBIP, vol. 256, pp. 501–514. Springer, Cham (2016). doi: 10.1007/978-3-319-42887-1_40 CrossRefGoogle Scholar
  6. 6.
    Baumgrass, A., Cabanillas, C., Di Ciccio, C.: A conceptual architecture for an event-based information aggregation engine in smart logistics. In: EMISA, pp. 109–123. GI (2015)Google Scholar
  7. 7.
    Baumgrass, A., Herzberg, N., Meyer, A., Weske, M.: BPMN extension for business process monitoring. In: EMISA 2014, pp. 85–98. GI (2014)Google Scholar
  8. 8.
    Cabanillas, C., Di Ciccio, C., Mendling, J., Baumgrass, A.: Predictive task monitoring for business processes. In: Sadiq, S., Soffer, P., Völzer, H. (eds.) BPM 2014. LNCS, vol. 8659, pp. 424–432. Springer, Cham (2014). doi: 10.1007/978-3-319-10172-9_31 Google Scholar
  9. 9.
    Di Ciccio, C., van der Aa, H., Cabanillas, C., Mendling, J., Prescher, J.: Detecting flight trajectory anomalies and predicting diversions in freight transportation. Decis. Support Syst. 88, 1–17 (2016)CrossRefGoogle Scholar
  10. 10.
    Dumas, M., La Rosa, M., Mendling, J., Reijers, H.A.: Fundamentals of Business Process Management. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  11. 11.
    Eshuis, R., Van Gorp, P.: Synthesizing data-centric models from business process models. Computing 98(4), 1–29 (2015)MathSciNetGoogle Scholar
  12. 12.
    Gilley, K.M., Rasheed, A.: Making more by doing less: an analysis of outsourcing and its effects on firm performance. J. Manage. 26(4), 763–790 (2000)Google Scholar
  13. 13.
    Gnimpieba, Z.D.R., Nait-Sidi-Moh, A., Durand, D., Fortin, J.: Using internet of things technologies for a collaborative supply chain: application to tracking of pallets and containers. Procedia Comput. Sci. 56, 550–557 (2015)CrossRefGoogle Scholar
  14. 14.
    Herzberg, N., Meyer, A., Weske, M.: Improving business process intelligence by observing object state transitions. Data Knowl. Eng. 98, 144–164 (2015)CrossRefGoogle Scholar
  15. 15.
    Hull, R., Damaggio, E., Fournier, F., Gupta, M., Heath, F.T., Hobson, S., Linehan, M., Maradugu, S., Nigam, A., Sukaviriya, P., Vaculin, R.: Introducing the guard-stage-milestone approach for specifying business entity lifecycles. In: Bravetti, M., Bultan, T. (eds.) WS-FM 2010. LNCS, vol. 6551, pp. 1–24. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-19589-1_1 CrossRefGoogle Scholar
  16. 16.
    Köpke, J., Su, J.: Towards quality-aware translations of activity-centric processes to guard stage milestone. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNCS, vol. 9850, pp. 308–325. Springer, Cham (2016). doi: 10.1007/978-3-319-45348-4_18 CrossRefGoogle Scholar
  17. 17.
    Liu, R., Vaculín, R., Shan, Z., Nigam, A., Wu, F.: Business artifact-centric modeling for real-time performance monitoring. In: Rinderle-Ma, S., Toumani, F., Wolf, K. (eds.) BPM 2011. LNCS, vol. 6896, pp. 265–280. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-23059-2_21 CrossRefGoogle Scholar
  18. 18.
    Maamar, Z., Faci, N., Sellami, M., Boukadi, K., Yahya, F., Barnawi, A., Sakr, S.: On business process monitoring using cross-flow coordination. Serv. Oriented Comput. Appl. 11(2), 203–215 (2017)CrossRefGoogle Scholar
  19. 19.
    Meijler, T.D., Stollberg, M., Winkler, M., Erler, K.: Coordinating variable collaboration processes in logistics. In: MITIP 2011 (2011)Google Scholar
  20. 20.
    Meroni, G., Di Ciccio, C., Mendling, J.: Artifact-driven process monitoring: dynamically binding real-world objects to running processes. In: CAiSE 2017 Forum, pp. 105–112 (2017). CEUR-WS.org
  21. 21.
    Pufahl, L., Weske, M.: Batch processing across multiple business processes based on object life cycles. In: Abramowicz, W., Alt, R., Franczyk, B. (eds.) BIS 2016. LNBIP, vol. 255, pp. 195–208. Springer, Cham (2016). doi: 10.1007/978-3-319-39426-8_16 CrossRefGoogle Scholar
  22. 22.
    Richardson, L., Ruby, S.: RESTful Web Services - Web Services for the Real World. O’Reilly, Sebastopol (2007)Google Scholar
  23. 23.
    Telang, P.R., Singh, M.P.: Specifying and verifying cross-organizational business models: an agent-oriented approach. IEEE Trans. Serv. Comput. 5(3), 305–318 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Giovanni Meroni
    • 1
  • Claudio Di Ciccio
    • 2
  • Jan Mendling
    • 2
  1. 1.Dipartimento di Elettronica, Informazione e BioingegneriaPolitecnico di MilanoMilanItaly
  2. 2.Institute for Information BusinessVienna University of Economics and BusinessViennaAustria

Personalised recommendations