We put forward Answer Set Programming (ASP) as a solution approach for three classical problems in Declarative Process Mining: Log Generation, Query Checking, and Conformance Checking. These problems correspond to different ways of analyzing business processes under execution, starting from sequences of recorded events, a.k.a. event logs. We tackle them in their data-aware variant, i.e., by considering events that carry a payload (set of attribute-value pairs), in addition to the performed activity, specifying processes declaratively with an extension of linear-time temporal logic over finite traces (LTLf). The data-aware setting is significantly more challenging than the control-flow one: Query Checking is still open, while the existing approaches for the other two problems do not scale well. The contributions of the work include an ASP encoding schema for the three problems, their solution, and experiments showing the feasibility of the approach.
翻译:我们提出答案设置程序(ASP),作为申报过程采矿的三个典型问题的解决方案: 日志生成、 查询检查和合规检查。 这些问题与从记录事件顺序( a. k. a. event logs) 开始分析正在实施的业务流程的不同方法相对应。 我们用数据认知变量( 即考虑载荷( 属性- 价值配对集) 的事件) 来解决这些问题, 除了执行的活动之外, 还要具体指明以线性时间逻辑延伸超过有限痕迹( LTLf ) 的过程。 数据认知设置比控制- 流程( 控制- 流程一) 挑战性要大得多 : 查询检查仍然开放, 而其他两个问题的现有处理方法没有很好的规模。 工作的贡献包括用于三个问题及其解决方案的 ASP编码模型, 以及实验显示方法的可行性。