Skip to main content
Log in

A process-mining-based scenarios generation method for SOA application development

  • Original Research Paper
  • Published:
Service Oriented Computing and Applications Aims and scope Submit manuscript

Abstract

Business process models which are usually constructed by business designers from experience and analysis are the main guidelines for services composition in the service-oriented architecture (SOA) applications development. However, due to the complexity of business models, it is a challenging task for business process designers to optimize the process models dynamically in accordance with changes in business environments. In this paper, a process-mining-based method is proposed to support business process designers to monitor efficiency or capture the changes of a business process. Firstly, we define a scenario model to depict business elements and their relationships which are critical to business process design. Based on the proposed scenario model, process mining algorithms, including control flow mining, roles mining and data flow mining are carried out in a certain sequence synthetically to extract business scenarios from event logs recorded by SOA application systems. Finally, we implement a prototype using a logistic scenario to illustrate the feasibility of our method in SOA applications development.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Van Der Aalst WM (2013) Business process management: a comprehensive survey. ISRN Software Engineering: Article ID 507984. doi:10.1155/2013/507984

  2. Sheng QZ et al (2014) Web services composition: a decade’s overview. Inf Sci 280:218–238

    Article  Google Scholar 

  3. van der Aalst WM (2013) Business process management: a comprehensive survey. ISRN Software Engineering, vol 2013, Article ID 507984. doi:10.1155/2013/507984

  4. Jimin L, Feng Q, Zhang L (2014) A business process simulation method supporting resource evolution. In: Proceedings of the 2014 international conference on software and system process, pp 169–177

  5. Van Der Aalst W, Zhao JL, Wang HJ (2015) Editorial: business process intelligence: connecting data and processes. ACM Trans Manag Inf Syst (TMIS) 5(4):181e:1–181e:5

    Google Scholar 

  6. Duan Q et al (2012) A survey on service-oriented network virtualization toward convergence of networking and cloud computing. IEEE Trans Netw Serv Manag 9(4):373–392

    Article  Google Scholar 

  7. Yao JT et al (2013) Granular computing: perspectives and challenges. IEEE Trans Cybern 43(6):1977–1989

    Article  Google Scholar 

  8. Wang Q, Yung KL, Ip WH (2004) A hierarchical multi-view modeling for Networked Joint Manufacturing System. Comput Ind 53(1):59–73

    Article  Google Scholar 

  9. Kwakkel JH, Auping WL, Pruyt E (2013) Dynamic scenario discovery under deep uncertainty: the future of copper. Technol Forecast Soc Chang 80(4):789–800

    Article  Google Scholar 

  10. Maghouli P, Hosseini SH, Oloomi Buygi M, Shahidehpour M (2011) A scenario-based multi-objective model for multi-stage transmission expansion planning. IEEE Trans Power Syst 26(1):470–478

    Article  Google Scholar 

  11. Ruokonen A, Kokko T, Systa T (2012) Scenario-driven approach for business process development. Int J Bus Process Integr Manag 6(1):77–96

    Article  Google Scholar 

  12. De Weerdt J, Schupp A, Vanderloock A, Baesens B (2013) Process Mining for the multi-faceted analysis of business processes—a case study in a financial services organization. Comput Ind 64:57–67

    Article  Google Scholar 

  13. Rovani M, Maggi FM, de Leoni M, van der Aalst WM (2015) Declarative process mining in healthcare. Expert Syst Appl 42:9236–9251

    Article  Google Scholar 

  14. Folino F, Greco G, Guzzo A, Pontieri L (2011) Mining usage scenarios in business processes: outlier-aware discovery and run-time prediction. Data Knowl Eng 70:1005–1029

    Article  Google Scholar 

  15. Li C, Reichert M, Wombacher A (2011) Mining business process variants: challenges, scenarios, algorithms. Data Knowl Eng 70:409–434

    Article  Google Scholar 

  16. Baier T, Mendling J, Weske M (2014) Bridging abstraction layers in process mining. Inf Syst 46:123–139

    Article  Google Scholar 

  17. Liu Y, Zhang H, Li C, Jiao RJ (2012) Workflow simulation for operational decision support using event graph through process mining. Decis Support Syst 52:685–697

    Article  Google Scholar 

  18. Partington A, Wynn M, Suriadi S, Ouyang C, Karnon J (2015) Process mining for clinical processes: a comparative analysis of four Australian Hospitals. ACM Trans Manag Inf Syst 5(4):Article 19

  19. De Leoni M, Van Der Aalst WM (2013) Data-aware process mining: discovering decisions in processes using alignments. In: Proceedings of the 28th annual acm symposium on applied computing. pp 1454–1461

  20. van der Aalst W (2013) Service mining: using process mining to discover, check, and improve service behavior. IEEE Trans Serv Comput 6(4):525–535

    Article  Google Scholar 

  21. Gaaloul W, Baïna K, Godart C (2008) Log-based mining techniques applied to web service composition reengineering. SOCA 2:93–110. doi:10.1007/s11761-008-0023-6

    Article  Google Scholar 

  22. Chesbrough H, Spohrer J (2006) A research manifesto for services science. Commun ACM 49(7):35–40

    Article  Google Scholar 

  23. Grabowik C, Knosala R (2003) The method of knowledge representation for a CAPP system. J Mater Process Technol 133(1):90–98

    Article  Google Scholar 

  24. Yu H, Zhu C, Cai H, Xu B (2009) Role-centric RESTful services description and composition for E-business applications. In: Proceedings of IEEE international conference on e-business engineering. pp 103–110

  25. Gunther CW, Rinderle-Ma S, Reichert M, van der Aalst WM, Recker J (2008) Using process mining to learn from process changes in evolutionary systems. Int J Bus Process Integr Manag 3(1):61–78

    Article  Google Scholar 

  26. Van Der Aalst WM, Dumas M, Ouyang C, Rozinat A, Verbeek E (2008) Conformance checking of service behavior. ACM Trans Internet Technol 8(3):Article No. 13

  27. Diniz PC, Ferreira DR (2008) Automatic extraction of process control flow from I/O operations. In: Proceedings of 6th international conference business process management. pp 342–357

  28. Van der Aalst W, Weijters T, Maruster L (2004) Workflow mining: discovering process models from event logs. IEEE Trans Knowl Data Eng 16(9):1128–1142

    Article  Google Scholar 

  29. Ernst MD, Cockrell J, Griswold WG, Notkin D (2001) Dynamically discovering likely program invariants to support program evolution. IEEE Trans Softw Eng 27(2):99–123

    Article  Google Scholar 

  30. Rahimi MR et al (2014) Mobile cloud computing: a survey, state of art and future directions. MONET 19(2):133–143

    Google Scholar 

  31. Rahimi MR et al (2013) MuSIC: mobility-aware optimal service allocation in mobile cloud computing. IEEE CLOUD 2013:75–82

    Google Scholar 

  32. Wei G et al (2010) A game-theoretic method of fair resource allocation for cloud computing services. J Supercomput 54(2):252–269

    Article  Google Scholar 

  33. Verbeek HMW, Buijs Joos CAM, Van Dongen BF, Van Der Aalst WM (2011) Xes, xesame, and prom 6. Information Systems Evolution. Lecture Notes in Business Information Processing 72:60–75

  34. Repa V, Železník O (2014) Methodological limitations of modeling languages BPMN and ARIS. In: The proceedings of 15th international carpathian control conference. pp 507–512

  35. Mens T, D’Hondt T (2000) Automating support for software evolution in UML. Autom Softw Eng 7(1):39–59

    Article  Google Scholar 

Download references

Acknowledgments

This research is supported by the National Natural Science Foundation of China under Nos. 61373030, 71171132.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hongming Cai.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jiang, L., Wang, J., Shah, N. et al. A process-mining-based scenarios generation method for SOA application development. SOCA 10, 303–315 (2016). https://doi.org/10.1007/s11761-015-0188-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11761-015-0188-8

Keywords

Navigation