Crowd-Based Mining of Reusable Process Model Patterns

  • Carlos Rodríguez
  • Florian Daniel
  • Fabio Casati
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8659)

Abstract

Process mining is a domain where computers undoubtedly outperform humans. It is a mathematically complex and computationally demanding problem, and event logs are at too low a level of abstraction to be intelligible in large scale to humans. We demonstrate that if instead the data to mine from are models (not logs), datasets are small (in the order of dozens rather than thousands or millions), and the knowledge to be discovered is complex (reusable model patterns), humans outperform computers. We design, implement, run, and test a crowd-based pattern mining approach and demonstrate its viability compared to automated mining. We specifically mine mashup model patterns (we use them to provide interactive recommendations inside a mashup tool) and explain the analogies with mining business process models. The problem is relevant in that reusable model patterns encode valuable modeling and domain knowledge, such as best practices or organizational conventions, from which modelers can learn and benefit when designing own models.

Keywords

Model patterns Pattern mining Crowdsourcing Mashups 

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References

  1. 1.
    Daniel, F., Matera, M.: Mashups: Concepts, Models and Architectures. Springer (2014)Google Scholar
  2. 2.
    Geng, L., Hamilton, H.: Interestingness measures for data mining: A survey. ACM Computing Surveys 38(3), 9 (2006)CrossRefGoogle Scholar
  3. 3.
    Greco, G., Guzzo, A., Manco, G., Sacca, D.: Mining and reasoning on workflows. IEEE Transactions on Knowledge and Data Engineering 17(4), 519–534 (2005)CrossRefGoogle Scholar
  4. 4.
    Gschwind, T., Koehler, J., Wong, J.: Applying Patterns during Business Process Modeling. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 4–19. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  5. 5.
    Günther, C.W., van der Aalst, W.M.P.: Fuzzy mining – adaptive process simplification based on multi-perspective metrics. In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 328–343. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Howe, J.: Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business, 1st edn. Crown Publishing Group, New York (2008)Google Scholar
  7. 7.
    Klinkmüller, C., Weber, I., Mendling, J., Leopold, H., Ludwig, A.: Increasing Recall of Process Model Matching by Improved Activity Label Matching. In: Daniel, F., Wang, J., Weber, B. (eds.) BPM 2013. LNCS, vol. 8094, pp. 211–218. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  8. 8.
    Lau, J.M., Iochpe, C., Thom, L., Reichert, M.: Discovery and analysis of activity pattern cooccurrences in business process models. In: ICEIS (2009)Google Scholar
  9. 9.
    Li, C., Reichert, M., Wombacher, A.: Discovering reference models by mining process variants using a heuristic approach. In: Dayal, U., Eder, J., Koehler, J., Reijers, H.A. (eds.) BPM 2009. LNCS, vol. 5701, pp. 344–362. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  10. 10.
    Li, W., Seshia, S.A., Jha, S.: CrowdMine: towards crowdsourced human-assisted verification. In: DAC, pp. 1250–1251. IEEE (2012)Google Scholar
  11. 11.
    Rodríguez, C., Chowdhury, S.R., Daniel, F., Nezhad, H.R.M., Casati, F.: Assisted Mashup Development: On the Discovery and Recommendation of Mashup Composition Knowledge. In: Web Services Foundations, pp. 683–708 (2014)Google Scholar
  12. 12.
    Roy Chowdhury, S., Daniel, F., Casati, F.: Recommendation and Weaving of Reusable Mashup Model Patterns for Assisted Development. ACM Trans. Internet Techn. (2014) (in print)Google Scholar
  13. 13.
    Roy Chowdhury, S., Rodríguez, C., Daniel, F., Casati, F.: Baya: assisted mashup development as a service. In: WWW Companion, pp. 409–412. ACM (2012)Google Scholar
  14. 14.
    Sheng, V.S., Provost, F., Ipeirotis, P.G.: Get another label? improving data quality and data mining using multiple, noisy labelers. In: SIGKDD, pp. 614–622. ACM (2008)Google Scholar
  15. 15.
    van der Aalst, W.M.P., Weijters, T., Maruster, L.: Workflow mining: Discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering 16(9), 1128–1142 (2004)CrossRefGoogle Scholar
  16. 16.
    van der Aalst, W.M.P., ter Hofstede, A.H.M., Kiepuszewski, B., Barros, A.P.: Workflow patterns. Distributed and Parallel Databases 14(1), 5–51 (2003)CrossRefGoogle Scholar
  17. 17.
    Von Ahn, L., Dabbish, L.: Labeling images with a computer game. In: SIGCHI, pp. 319–326. ACM (2004)Google Scholar
  18. 18.
    Weijters, A., van der Aalst, W.M.P., De Medeiros, A.A.: Process mining with the heuristics miner-algorithm. TU Eindhoven, Tech. Rep. WP, 166 (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Carlos Rodríguez
    • 1
  • Florian Daniel
    • 1
  • Fabio Casati
    • 1
  1. 1.University of TrentoPovoItaly

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