Episode mining is a research area in data mining, where the aim is to discover interesting episodes, that is, subsequences of events, in an event sequence. The most popular episode-mining task is frequent episode mining (FEM), which consists of identifying episodes that appear frequently in an event sequence, but this task has also been extended in various ways. It was shown that episode mining can reveal insightful patterns for numerous applications such as web stream analysis, network fault management, and cybersecurity, and that episodes can be useful for prediction. Episode mining is an active research area, and there have been numerous advances in the field over the last 25 years. However, due to the rapid evolution of the pattern mining field, there is no prior study that summarizes and gives a detailed overview of this field. The contribution of this article is to fill this gap by presenting an up-to-date survey that provides an introduction to episode mining and an overview of recent developments and research opportunities. This advanced review first gives an introduction to the field of episode mining and the first algorithms. Then, the main concepts used in these algorithms are explained. After that, several recent studies are reviewed that have addressed some limitations of these algorithms and proposed novel solutions to overcome them. Finally, the paper lists some possible extensions of the existing frameworks to mine more meaningful patterns and presents some possible orientations for future work that may contribute to the evolution of the episode mining field.

中文翻译：

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剧集挖掘综述

情节挖掘是数据挖掘中的一个研究领域，其目的是发现事件序列中有趣的情节，即事件的子序列。最流行的情节挖掘任务是频繁情节挖掘（FEM），它包括识别事件序列中频繁出现的情节，但该任务也以各种方式进行了扩展。研究表明，事件挖掘可以揭示网络流分析、网络故障管理和网络安全等众多应用的富有洞察力的模式，并且事件对于预测很有用。集挖掘是一个活跃的研究领域，在过去 25 年中该领域取得了许多进展。然而，由于模式挖掘领域的快速发展，之前没有研究对该领域进行总结和详细概述。本文的贡献是通过提供最新的调查来填补这一空白，该调查提供了事件挖掘的介绍以及最近的发展和研究机会的概述。这篇高级评论首先介绍了情节挖掘领域和第一个算法。然后，解释这些算法中使用的主要概念。之后，回顾了最近的几项研究，这些研究解决了这些算法的一些局限性，并提出了克服这些局限性的新解决方案。最后，本文列出了现有框架的一些可能的扩展，以挖掘更有意义的模式，并提出了未来工作的一些可能的方向，这些方向可能有助于情节挖掘领域的发展。