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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Daniel, F., Matera, M.: Mashups: Concepts, Models and Architectures. Springer (2014)
Geng, L., Hamilton, H.: Interestingness measures for data mining: A survey. ACM Computing Surveys 38(3), 9 (2006)
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)
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)
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)
Howe, J.: Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business, 1st edn. Crown Publishing Group, New York (2008)
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)
Lau, J.M., Iochpe, C., Thom, L., Reichert, M.: Discovery and analysis of activity pattern cooccurrences in business process models. In: ICEIS (2009)
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)
Li, W., Seshia, S.A., Jha, S.: CrowdMine: towards crowdsourced human-assisted verification. In: DAC, pp. 1250–1251. IEEE (2012)
RodrÃguez, 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)
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)
Roy Chowdhury, S., RodrÃguez, C., Daniel, F., Casati, F.: Baya: assisted mashup development as a service. In: WWW Companion, pp. 409–412. ACM (2012)
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)
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)
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)
Von Ahn, L., Dabbish, L.: Labeling images with a computer game. In: SIGCHI, pp. 319–326. ACM (2004)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
RodrÃguez, C., Daniel, F., Casati, F. (2014). Crowd-Based Mining of Reusable Process Model Patterns. In: Sadiq, S., Soffer, P., Völzer, H. (eds) Business Process Management. BPM 2014. Lecture Notes in Computer Science, vol 8659. Springer, Cham. https://doi.org/10.1007/978-3-319-10172-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-10172-9_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10171-2
Online ISBN: 978-3-319-10172-9
eBook Packages: Computer ScienceComputer Science (R0)