Interactive Multi Interest Process Pattern Discovery

Mozhgan Vazifehdoostirani, Laura Genga, Xixi Lu, Rob Verhoeven, H. Laarhoven, R. Dijkman
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引用次数: 0

Abstract

Process pattern discovery methods (PPDMs) aim at identifying patterns of interest to users. Existing PPDMs typically are unsupervised and focus on a single dimension of interest, such as discovering frequent patterns. We present an interactive multi interest driven framework for process pattern discovery aimed at identifying patterns that are optimal according to a multi-dimensional analysis goal. The proposed approach is iterative and interactive, thus taking experts knowledge into account during the discovery process. The paper focuses on a concrete analysis goal, i.e., deriving process patterns that affect the process outcome. We evaluate the approach on real world event logs in both interactive and fully automated settings. The approach extracted meaningful patterns validated by expert knowledge in the interactive setting. Patterns extracted in the automated settings consistently led to prediction performance comparable to or better than patterns derived considering single interest dimensions without requiring user defined thresholds.
交互式多兴趣过程模式发现
流程模式发现方法(ppdm)旨在识别用户感兴趣的模式。现有的ppdm通常是无监督的,并且专注于单个感兴趣的维度,例如发现频繁的模式。我们提出了一个交互式的多兴趣驱动框架,用于过程模式发现,旨在根据多维分析目标识别最优模式。所提出的方法具有迭代性和交互性,因此在发现过程中考虑了专家知识。本文着重于一个具体的分析目标,即推导影响过程结果的过程模式。我们在交互式和全自动设置下对真实世界的事件日志方法进行了评估。该方法在交互设置中提取经专家知识验证的有意义的模式。在自动化设置中提取的模式的预测性能与考虑单一兴趣维度而不需要用户定义阈值的模式相当,甚至更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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