A Template for Categorizing Business Processes in Empirical Research

Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 297)

Abstract

Empirical Research is becoming increasingly important for understanding the practical uses and problems with business processes technology in the field. However, no standardization on how to report observations and findings exists. This sometimes leads to research outcomes which report partial or incomplete data and make published results of replicated studies on different data sets hard to compare. In order to help the research community improve reporting on business process models and collections and their characteristics, this paper defines a modular template with the aim of reports’ standardization, which could also facilitate the creation of shared business process repositories to foster further empirical research in the future. The template has been positively evaluated by representatives from both BPM research and industry. The survey feedback has been incorporated in the template. We have applied the template to describe a real-world executable WS-BPEL process collection, measured from a static and dynamic perspective.

Keywords

Empirical research Meta-data template Business process Business process description Business process metrics 

References

  1. 1.
    Alemneh, E., et al.: A static analysis tool for BPEL source codes. Int. J. Comput. Sci. Mob. Comput. 3(2), 659–665 (2014)Google Scholar
  2. 2.
    Berli, W., Lübke, D., Möckli, W.: Terravis - large scale business process integration between public and private partners. In: Plödereder, E., Grunske, L., Schneider, E., Ull, D. (eds.) Lecture Notes in Informatics (LNI), vol. P-232, pp. 1075–1090. Gesellschaft für Informatik e.V. (2014)Google Scholar
  3. 3.
    Cardoso, J.: Complexity analysis of BPEL web processes. Softw. Process Improv. Pract. J. 12, 35–49 (2006)CrossRefGoogle Scholar
  4. 4.
    Cardoso, J.: Business process control-flow complexity: Metric, evaluation, and validation. Int. J. Web Serv. Res. (IJWSR) 5(2), 49–76 (2008)CrossRefGoogle Scholar
  5. 5.
    Cardoso, J., Mendling, J., Neumann, G., Reijers, H.A.: A discourse on complexity of process models. In: Eder, J., Dustdar, S. (eds.) BPM 2006. LNCS, vol. 4103, pp. 117–128. Springer, Heidelberg (2006). doi:10.1007/11837862_13 CrossRefGoogle Scholar
  6. 6.
    Eid-Sabbagh, R.-H., Kunze, M., Meyer, A., Weske, M.: A platform for research on process model collections. In: Mendling, J., Weidlich, M. (eds.) BPMN 2012. LNBIP, vol. 125, pp. 8–22. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33155-8_2 CrossRefGoogle Scholar
  7. 7.
    Executive Office of the President - Office of Management, Budget: North American Industry Classification System (2017). http://census.gov/naics
  8. 8.
    Hertis, M., Juric, M.B.: An empirical analysis of business process execution language usage. IEEE Trans. Softw. Eng. 40(08), 738–757 (2014)CrossRefGoogle Scholar
  9. 9.
    Houy, C., Fettke, P., Loos, P.: Empirical research in business process management-analysis of an emerging field of research. Bus. Process Manag. J. 16(4), 619–661 (2010)CrossRefGoogle Scholar
  10. 10.
    Jordan, D., et al.: Web Services Business Process Execution Language Version 2.0. OASIS, April 2007Google Scholar
  11. 11.
    Lübke, D.: Using metric time lines for identifying architecture shortcomings in process execution architectures. In: 2015 IEEE/ACM 2nd International Workshop on Software Architecture and Metrics (SAM), pp. 55–58. IEEE (2015)Google Scholar
  12. 12.
    Mao, C.: Control and data complexity metrics for web service compositions. In: Proceedings of the 10th International Conference on Quality Software 2010 (2010)Google Scholar
  13. 13.
    Mendling, J.: Metrics for Process Models: Empirical Foundations of Verification, Error Prediction, and Guidelines for Correctness, 1 edn. Springer (2008)Google Scholar
  14. 14.
    Mendling, J.: Empirical studies in process model verification. In: Jensen, K., Aalst, W.M.P. (eds.) Transactions on Petri Nets and Other Models of Concurrency II. LNCS, vol. 5460, pp. 208–224. Springer, Heidelberg (2009). doi:10.1007/978-3-642-00899-3_12 CrossRefGoogle Scholar
  15. 15.
    Skouradaki, M., Roller, D., Pautasso, C., Leymann, F.: “bpelanon”: Anonymizing bpel processes. In: ZEUS, pp. 1–7. Citeseer (2014)Google Scholar
  16. 16.
    Vanhatalo, J., Koehler, J., Leymann, F.: Repository for business processes and arbitrary associated metadata. In: Proceedings of the Demo Session of the 4th International Conference on Business Process Management (2006)Google Scholar
  17. 17.
    Weber, B., Mutschler, B., Reichert, M.: Investigating the effort of using business process management technology: results from a controlled experiment. Sci. Comput. Program. 75(5), 292–310 (2010)CrossRefGoogle Scholar
  18. 18.
    Wetzstein, B., Strauch, S., Leymann, F.: Measuring performance metrics of WS-BPEL service compositions. In: Proceedings of ICNS, pp. 49–56 (2009)Google Scholar
  19. 19.
    Wohlin, C., Runeson, P., Höst, M., Ohlsson, M.C., Regnell, B., Wesslén, A.: Experimentation in Software Engineering. Springer, Heidelberg (2012)Google Scholar
  20. 20.
    Yan, Z., Dijkman, R., Grefen, P.: Business process model repositories-framework and survey. Inf. Softw. Technol. 54(4), 380–395 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Daniel Lübke
    • 1
    • 2
  • Ana Ivanchikj
    • 3
  • Cesare Pautasso
    • 3
  1. 1.innoQ Schweiz GmbHChamSwitzerland
  2. 2.FG Software Engineering, Leibniz Universität HannoverHanoverGermany
  3. 3.Software Institute, Faculty of InformaticsUSI LuganoLuganoSwitzerland

Personalised recommendations