A data-driven process recommender framework
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Emergency medical process analysis; Process prototype extraction; Process recommender system; Process trace clustering
© 2017 Association for Computing Machinery. We present an approach for improving the performance of complex knowledge-based processes by providing data-driven step-by-step recommendations. Our framework uses the associations between similar historic process performances and contextual information to determine the prototypical way of enacting the process. We introduce a novel similarity metric for grouping traces into clusters that incorporates temporal information about activity performance and handles concurrent activities. Our data-driven recommender system selects the appropriate prototype performance of the process based on userprovided context attributes. Our approach for determining the prototypes discovers the commonly performed activities and their temporal relationships. We tested our system on data from three real-world medical processes and achieved recommendation accuracy up to an F1 score of 0.77 (compared to an F1 score of 0.37 using ZeroR) with 63.2% of recommended enactments being within the first five neighbors of the actual historic enactments in a set of 87 cases. Our framework works as an interactive visual analytic tool for process mining. This work shows the feasibility of data-driven decision support system for complex knowledge-based processes.
Yang, S., Dong, X., Sun, L., Zhou, Y., Farneth, R., Xiong, H., Burd, R., & Marsic, I. (2017). A data-driven process recommender framework. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Part F129685 (). http://dx.doi.org/10.1145/3097983.3098174