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：决策挖掘能够从事件日志或流中发掘决策规则，是业务流程深度分析与优化的重要组成部分。迄今为止，决策挖掘仅以事后方式应用，从而导致针对给定日志数据片段仅能得到决策规则快照。相比之下，在线决策挖掘可实现决策规则演化与决策漂移的持续监控。为此，本文提出一种端到端方法，能够在运行时同时实现决策点及对应决策规则的发现与监控，从而弥合在线控制流发现与决策挖掘之间的鸿沟。该方法为过程感知信息系统提供自动决策支持，具备高效的决策漂移发现与监控能力。在监控过程中，不仅考虑决策规则在准确性方面的性能表现，还关注数据要素的出现以及分支频率的变化。本文提出两种算法，并在四个合成数据集与一个真实数据集上进行评估，验证了方法的可行性与适用性。总体而言，该方法促进了对业务流程中决策的理解，从而有助于改进人-过程交互。