Crowd-Based Mining of Reusable Process Model Patterns
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.
KeywordsModel patterns Pattern mining Crowdsourcing Mashups
Unable to display preview. Download preview PDF.
- 1.Daniel, F., Matera, M.: Mashups: Concepts, Models and Architectures. Springer (2014)Google Scholar
- 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
- 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
- 10.Li, W., Seshia, S.A., Jha, S.: CrowdMine: towards crowdsourced human-assisted verification. In: DAC, pp. 1250–1251. IEEE (2012)Google Scholar
- 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.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.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.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
- 17.Von Ahn, L., Dabbish, L.: Labeling images with a computer game. In: SIGCHI, pp. 319–326. ACM (2004)Google Scholar
- 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