论文标题
从事件日志中发现生成模型：数据驱动的模拟与深度学习
Discovering Generative Models from Event Logs: Data-driven Simulation vs Deep Learning
论文作者
论文摘要
生成模型是一个统计模型，能够从先前观察到的模型中生成新的数据实例。在业务流程的背景下，生成模型从一组历史痕迹（也称为事件日志）创建新的执行痕迹。先前的工作中已经开发了两个生成过程模拟模型的家族：数据驱动的仿真模型和深度学习模型。到目前为止，这两种方法已经独立发展，并且尚未研究其相对性能。本文通过经验将数据驱动的仿真技术与多种深度学习技术进行经验比较，构建模型能够生成具有时间戳事件的执行轨迹，从而填补了这一空白。该研究阐明了两种方法的相对强度，并提高了结合这些优势的混合方法的前景。
A generative model is a statistical model that is able to generate new data instances from previously observed ones. In the context of business processes, a generative model creates new execution traces from a set of historical traces, also known as an event log. Two families of generative process simulation models have been developed in previous work: data-driven simulation models and deep learning models. Until now, these two approaches have evolved independently and their relative performance has not been studied. This paper fills this gap by empirically comparing a data-driven simulation technique with multiple deep learning techniques, which construct models are capable of generating execution traces with timestamped events. The study sheds light into the relative strengths of both approaches and raises the prospect of developing hybrid approaches that combine these strengths.
