Case and Activity Identification for Mining Process Models from Middleware
Case and Activity Identification for Mining Process Models from Middleware
复制标题
中间件挖掘过程模型的案例和活动识别
DOI：
10.1007/978-3-030-02302-7_6
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Peter Queteschiner
中科院分区：
文献类型：
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作者：
Saimir Bala;J. Mendling;M. Schimak;Peter Queteschiner
流程监控旨在提供业务流程的操作方面的透明度。在实践中，业务流程执行的跟踪跨越许多不同的系统是一个挑战。识别非结构化事件数据中的哪些属性可以作为案例和活动标识符来提取和监视业务流程，这是一项繁琐的手动工程工作。文献中的方法假设这些标识符是先验已知的，并且数据可以以可扩展事件流（XES）等格式随时可用。然而，在实践中，情况几乎不是这样的，特别是当来自不同来源的事件数据在事件存储中被汇集在一起时。在本文中，我们解决了这个研究差距推断潜在的情况下，活动标识符的出处不可知的方式。更具体地说，我们提出了一个半自动的技术，发现语义相关的业务流程监控的事件关系。在一个国际电信供应商的行业案例研究的结果进行评估。
Process monitoring aims to provide transparency over operational aspects of a business process. In practice, it is a challenge that traces of business process executions span across a number of diverse systems. It is cumbersome manual engineering work to identify which attributes in unstructured event data can serve as case and activity identifiers for extracting and monitoring the business process. Approaches from literature assume that these identifiers are known a priori and data is readily available in formats like eXtensible Event Stream (XES). However, in practice this is hardly the case, specifically when event data from different sources are pooled together in event stores. In this paper, we address this research gap by inferring potential case and activity identifiers in a provenance agnostic way. More specifically, we propose a semi-automatic technique for discovering event relations that are semantically relevant for business process monitoring. The results are evaluated in an industry case study with an international telecommunication provider.