Decision mining enables the discovery of decision rules from event logs or streams, and constitutes an important part of in-depth analysis and optimisation of business processes. So far, decision mining has been merely applied in an ex-post way resulting in a snapshot of decision rules for the given chunk of log data. Online decision mining, by contrast, enables continuous monitoring of decision rule evolution and decision drift. Hence this paper presents an end-to-end approach for the discovery as well as monitoring of decision points and the corresponding decision rules during runtime, bridging the gap between online control flow discovery and decision mining. The approach provides automatic decision support for process-aware information systems with efficient decision drift discovery and monitoring. For monitoring, not only the performance, in terms of accuracy, of decision rules is taken into account, but also the occurrence of data elements and changes in branching frequency. The paper provides two algorithms, which are evaluated on four synthetic and one real-life data set, showing feasibility and applicability of the approach. Overall, the approach fosters the understanding of decisions in business processes and hence contributes to an improved human-process interaction.
翻译:决定采矿使得能够从事件日志或流程中发现决定规则,并成为深入分析和优化业务流程的一个重要部分。迄今为止,决定采矿只是事后应用,结果只对某一块记录数据提供了决定规则的快照。在线决策采矿则使得能够持续监测决定规则的演变和决定的漂移。因此,本文件介绍了在运行期间发现和监测决定点和相应的决定规则的端对端办法,缩小了在线控制流动发现和决定采矿之间的差距。该办法为具有流程意识的信息系统提供了自动决策支持,并高效地发现和监测了决定漂移。对于监测而言,不仅考虑到决策规则的准确性,而且还考虑到数据要素的出现和分支频率的变化。该文件提供了两种算法,这些算法对四个合成数据和一个真实寿命数据集进行了评估,显示了该方法的可行性和适用性。总体而言,该方法促进了对业务流程决策的理解,从而有助于改善人类流程的互动。</s>