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Granularity as a Cognitive Factor in the Effectiveness of Business Process Model Reuse

  • Oliver Holschke
  • Jannis Rake
  • Olga Levina
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5701)

Abstract

Reusing design models is an attractive approach in business process modeling as modeling efficiency and quality of design outcomes may be significantly improved. However, reusing conceptual models is not a cost-free effort, but has to be carefully designed. While factors such as psychological anchoring and task-adequacy in reuse-based modeling tasks have been investigated, information granularity as a cognitive concept has not been at the center of empirical research yet. We hypothesize that business process granularity as a factor in design tasks under reuse has a significant impact on the effectiveness of resulting business process models. We test our hypothesis in a comparative study employing high and low granularities. The reusable processes provided were taken from widely accessible reference models for the telecommunication industry (enhanced Telecom Operations Map). First experimental results show that Recall in tasks involving coarser granularity is lower than in cases of finer granularity. These findings suggest that decision makers in business process management should be considerate with regard to the implementation of reuse mechanisms of different granularities. We realize that due to our small sample size results are not statistically significant, but this preliminary run shows that it is ready for running on a larger scale.

Keywords

Business Process Model Reuse Reuse Economics Process Granularity Design for Reuse Reuse of Non-Code Artifacts Experiment 

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References

  1. 1.
    Yao, Y.: Probabilistic approaches to rough sets. Expert Systems 20, 287–297 (2003)CrossRefGoogle Scholar
  2. 2.
    Frakes, W., Kang, K.: Software reuse research: status and future. IEEE Transactions on Software Engineering 31, 529–536 (2005)CrossRefGoogle Scholar
  3. 3.
    Frakes, W.B., Terry, C.: Software Reuse: Metrics and Models. ACM Comput. Surv. 28, 415–435 (1996)CrossRefGoogle Scholar
  4. 4.
    Fischer, G.: Cognitive View of Reuse and Redesign. IEEE Software 4, 60–72 (1987)CrossRefGoogle Scholar
  5. 5.
    Ye, Y., Fischer, G.: Supporting Reuse by Delivering Task-Relevant and Personalized Information. In: International Conference on Software Engnineering (ICSE 2002). ACM, Orlando (2002)Google Scholar
  6. 6.
    Purao, S., Storey, V.C., Han, T.: Improving Analysis Pattern Reuse in Conceptual Design: Augmenting Automated Processes with Supervised Learning. Information Systems Research 14, 269–290 (2003)CrossRefGoogle Scholar
  7. 7.
    Szyperski, C.: Component Software: Beyond Object-Oriented Programming. Addison-Wesley, New York (1998)Google Scholar
  8. 8.
    vom Brocke, J., Buddendick, C.: Reusable Conceptual Models – Requirements Based on the Design Science Research Paradigm. In: Chen, H., Olfman, L., Hevner, A., Chatterjee, S. (eds.) Design Science Research in Information Systems and Technology (DESRIST 2006), Claremont, CA (2006)Google Scholar
  9. 9.
    Kelly, M.: Enhanced Telecom Operations Map (eTOM) - The Business Process Framework. TeleManagement Forum (2007)Google Scholar
  10. 10.
    Supply-Chain Council: Supply Chain Operations Reference-model Version 8.0. Supply-Chain Council, Inc. (2006)Google Scholar
  11. 11.
    Zimmermann, O., Gschwind, T., Küster, J.M., Leymann, F., Schuster, N.: Reusable Architectural Decision Models for Enterprise Application Development. In: Overhage, S., Szyperski, C.A., Reussner, R., Stafford, J.A. (eds.) Third International Conference on Quality of Software Architectures, Software Architectures, Components, and Applications (QoSA 2007), pp. 15–32. Springer, Medford (2007)CrossRefGoogle Scholar
  12. 12.
    Buckl, S., Ernst, A.M., Lankes, J., Schneider, K., Schweda, C.M.: A Pattern based Approach for constructing Enterprise Architecture Management Information Models. Internationale Tagung Wirtschaftsinformatik. Universitätsverlag Karlsruhe, Karlsruhe (2007)Google Scholar
  13. 13.
    Coad, P., North, D., Mayfield, M.: Object Models: Strategies, Patterns, Applications. Prentice-Hall, Englewood Cliffs (1996)Google Scholar
  14. 14.
    Gamma, E., Helm, R., Johnson, R., Vlissides, J.: Design Patterns: Elements of Reusable Object-Oriented Design. Addison-Wesley, Reading (1995)zbMATHGoogle Scholar
  15. 15.
    Zdun, U., Hentrich, C., Dustdar, S.: Modeling process-driven and service-oriented architectures using patterns and pattern primitives. ACM Transactions on the Web 1(3) (2007)Google Scholar
  16. 16.
    Sun, W., Zhang, X., Guo, C.J., Sun, P., Su, H.: Software as a Service: Configuration and Customization Perspectives. In: IEEE Congress on Services Part II. IEEE, Los Alamitos (2008)Google Scholar
  17. 17.
    Reijers, H.A., Mendling, J.: Modularity in process models: Review and effects. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 20–35. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  18. 18.
    Burton-Jones, A., Meso, P.: How good are these UML diagrams? An empirical test of the Wand and Weber good decomposition model. In: 23rd International Conference on Information Systems, Barcelona, pp. 15–18 (2002)Google Scholar
  19. 19.
    Wand, Y., Weber, R.: A model of systems decomposition. In: Tenth International Conference on Information Systems, Boston, MA, pp. 41–51 (1989)Google Scholar
  20. 20.
    Zadeh, L.A.: Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and Systems 19, 111–127 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Yao, Y.: A partition model of granular computing. In: Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B.z., Świniarski, R.W., Szczuka, M.S. (eds.) Transactions on Rough Sets I. LNCS, vol. 3100, pp. 232–253. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  22. 22.
    Shannon, C.E., Weaver, W.: The Mathematical Theory of Communication. University of Illinois Press (1963)Google Scholar
  23. 23.
    Parsons, J., Saunders, C.: Cognitive Heuristics in Software Engineering: Applying and Extending Anchoring and Adjustment to Artifact Reuse. IEEE Trans. Software Eng. 30, 873–888 (2004)CrossRefGoogle Scholar
  24. 24.
    Object Management Group: Business Process Modeling Notation Specification, Version 1.0 (2006)Google Scholar
  25. 25.
    Fettke, P., Loos, P.: Classification of reference models: a methodology and its application Information Systems and E-Business Management 1, 35–53 (2003)Google Scholar
  26. 26.
    Latva-Koivisto, A.M.: Finding a complexity measure for business process models. Helsinki University of Technology, Helsinki (2001)Google Scholar
  27. 27.
    Ehrig, M., Koschmider, A., Oberweis, A.: Measuring Similarity between Semantic Business Process Models. In: Fourth Asia-Pacific Conference on Conceptual Modelling (APCCM 2007). Australian Computer Society, Inc., Ballarat (2007)Google Scholar
  28. 28.
    Bosak, J., McGrath, T., Holman, G.K.: Universal Business Language v2.0. OASIS (2006)Google Scholar
  29. 29.
    Lindland, I., Sindre, G., Sølvberg, A.: Understanding quality in conceptual modeling. IEEE Software 11, 42–49 (1994)CrossRefGoogle Scholar
  30. 30.
    Moody, D.L., Sindre, G., Brasethvik, T., Sølvberg, A.: Evaluating the quality of process models: Empirical testing of a quality framework. In: Spaccapietra, S., March, S.T., Kambayashi, Y. (eds.) ER 2002. LNCS, vol. 2503, p. 380. Springer, Heidelberg (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Oliver Holschke
    • 1
  • Jannis Rake
    • 1
  • Olga Levina
    • 1
  1. 1.Fachgebiet Systemanalyse und EDVTechnische Universität BerlinBerlinGermany

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