AbstractMine: Privacy-aware Abstraction of Event Data for Distributed Process Mining

流程挖掘可以根据流程执行期间记录的事件对面向流程的系统进行分析。然而，现有技术通常假设事件表示作为单个案例的一部分的单个活动的执行；并且所有事件作为单个位置的日志的一部分可用。然而，一旦对来自传感器网络的事件采用过程挖掘，这些假设就不再有效。事件捕获低级信息，首先需要对其进行抽象，以便于根据要分析的流程解释它们。此外，事件源自分布式来源，这要求显式处理相关实体之间的数据交换。然而，事件抽象和数据交换也为解决流程挖掘中对隐私意识的新需求提供了机会。控制事件抽象的级别和位置，可以实现对所涉及的过程参与者的隐私的保证，而不会对要进行的分析强加任何假设。在AbstractMore中，我们的目标是提供算法基础，将隐私保证合并到由分布式源连续生成的事件的抽象中。这包括一种形式主义，用于描述事件的分布式抽象，并将其与对流程参与者隐私的攻击以及所产生的数据用于操作分析的效用相关联。在此基础上，我们提出了抽象事件的算法，在保证隐私的同时最大化数据效用。我们考虑将这些算法用于集中式和分布式设置，以及静态和流数据。Sourceed中的协作使我们能够将事件抽象链接到流程挖掘中的不确定性管理和可解释性方法，并实现可伸缩性，特别是在分布式应用场景中。

Process mining enables the analysis of process-oriented systems based on events recorded during their execution. However, existing techniques typically assume that an event denotes the execution of a single activity as part of a single case; and that all events are available as part of a log at a single location. Once process mining is adopted for events sourced from sensor networks, however, these assumptions are no longer valid. Events capture low-level information and first need to be abstracted to facilitate their interpretation in terms of the process to analyze. Also, events stem from distributed sources, which calls for explicit handling of data exchange between the involved entities. However, event abstraction and data exchange also open opportunities to address the emerging need for privacy-awareness in process mining. Controlling the level and location of event abstraction, guarantees on the privacy of involved process participants may be achieved, without imposing any assumption on the analysis to be conducted. In AbstractMine, we aim at providing the algorithmic foundations to incorporate privacy guarantees in the abstraction of events that are continuously generated by distributed sources. This includes a formalism to describe the distributed abstraction of events and link it to attacks on the privacy of process participants as well as the utility of the resulting data for operational analysis. Based thereon, we contribute algorithms for abstraction of events that give privacy guarantees, while maximizing the data utility. We consider these algorithms for centralized and distributed settings, as well as for static and streaming data. The collaborations within SOURCED enable us to link event abstractions to approaches for uncertainty management and explainability in process mining, as well as achieving scalability in particular in distributed application scenarios.