PLG2：在线和离线环境下的多视角过程随机化和模拟
PLG2: Multiperspective Processes Randomization and Simulation for Online and Offline Settings
摘要：
流程挖掘是BPM和数据采掘研究的一个重要领域。最近，它也获得了从业者的重要性：越来越多的公司正在创建业务流程智能解决方案。与其他数据采掘任务一样，流程挖掘算法的评估需要大量真实数据的可用性。尽管这些数据集的可用性越来越高，但它们受到许多限制的影响，首先是缺乏“黄金标准”（即，参考模型）。本文扩展了一种方法，已经在文献中，随机过程的生成。在整个工作中引入了新的内容，特别是，它们涉及对多视角模型和日志的完全支持（即，利用时间和数据信息来丰富控制流透视图）和用于在线设置（即，多视角事件流和概念漂移的生成）。拟议的新框架能够几乎完全涵盖在现实世界中可以观察到的各种可能情况。所提出的方法被实现为一个公开的Java应用程序，与一组API的编程执行的实验。
英文摘要：
Process mining represents an important field in BPM and data mining research. Recently, it has gained importance also for practitioners: more and more companies are creating business process intelligence solutions. The evaluation of process mining algorithms requires, as any other data mining task, the availability of large amount of real-world data. Despite the increasing availability of such datasets, they are affected by many limitations, in primis the absence of a "gold standard" (i.e., the reference model). This paper extends an approach, already available in the literature, for the generation of random processes. Novelties have been introduced throughout the work and, in particular, they involve the complete support for multiperspective models and logs (i.e., the control-flow perspective is enriched with time and data information) and for online settings (i.e., generation of multiperspective event streams and concept drifts). The proposed new framework is able to almost entirely cover the spectrum of possible scenarios that can be observed in the real-world. The proposed approach is implemented as a publicly available Java application, with a set of APIs for the programmatic execution of experiments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
