A Data-driven Process Recommender Framework.

Autor:	Yang S; Rutgers University, NJ., Dong X; Rutgers University, NJ., Sun L; Tsinghua University, PR China., Zhou Y; Rutgers University, NJ., Farneth RA; Children's Nat'l Medical Center, Washington, DC., Xiong H; Rutgers University, NJ., Burd RS; Children's Nat'l Medical Center, Washington, DC., Marsic I; Rutgers University, NJ.
Jazyk:	angličtina
Zdroj:	KDD : proceedings. International Conference on Knowledge Discovery & Data Mining [KDD] 2017 Aug; Vol. 2017, pp. 2111-2120.
DOI:	10.1145/3097983.3098174
Abstrakt:	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.
Databáze:	MEDLINE
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