| Title | A Data-driven Process Recommender Framework |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | Yang, Sen, Dong, Xin, Sun, Leilei, Zhou, Yichen, Farneth, Richard A., Xiong, Hui, Burd, Randall S., Marsic, Ivan |
| Conference Name | Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-4887-4 |
| Keywords | emergency medical process analysis., Measurement, Metrics, metrics testing, process prototype extraction, process recommender system, process trace clustering, pubcrawl |
| Abstract | 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 user-provided 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. |
| URL | http://doi.acm.org/10.1145/3097983.3098174 |
| DOI | 10.1145/3097983.3098174 |
| Citation Key | yang_data-driven_2017 |
A Data-driven Process Recommender Framework