A Data-driven Process Recommender Framework.
A Data-driven Process Recommender Framework.
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DOI：
10.1145/3097983.3098174
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发表时间：
2017-08
期刊：
影响因子：
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通讯作者：
Marsic I
中科院分区：
文献类型：
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作者：
Yang S;Dong X;Sun L;Zhou Y;Farneth RA;Xiong H;Burd RS;Marsic I
我们提出了一种方法，通过提供数据驱动的一步一步的建议，以提高复杂的基于知识的流程的性能。我们的框架使用类似的历史过程性能和上下文信息之间的关联，以确定制定的过程的原型方式。我们引入了一种新的相似性度量分组的轨迹到集群，结合活动性能的时间信息和处理并发活动。我们的数据驱动的推荐系统选择适当的原型性能的过程中，用户提供的上下文属性的基础上。我们的方法来确定原型发现通常执行的活动和它们的时间关系。我们在三个真实医疗过程的数据上测试了我们的系统，并实现了高达0.77的F1分数的推荐准确度（相比之下，使用ZeroR的F1分数为0.37），其中63.2%的推荐法规在87个案例中处于实际历史法规的前五个相邻区域内。我们的框架作为一个交互式的可视化分析工具，过程挖掘。这项工作表明了数据驱动的决策支持系统的复杂知识为基础的过程的可行性。
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.