Due to the complexity of the network structure, log analysis is usually necessary for the maintenance of network-based distributed systems since logs record rich information about the system behaviors. In recent years, numerous works have been proposed for log analysis; however, they ignore temporal relationships between logs. In this paper, we target on the problem of mining informative patterns from temporal log data. We propose an approach to discover sequential patterns from event sequences with temporal regularities. Discovered patterns are useful for engineers to understand the behaviors of a network-based distributed system. To solve the well-known problem of pattern explosion, we resort to the minimum description length (MDL) principle and take a step forward in summarizing the temporal relationships between adjacent events of a pattern. Experiments on real log datasets prove the efficiency and effectiveness of our method.

中文翻译：

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日志模式挖掘，用于分布式系统维护

由于网络结构的复杂性，日志分析通常是维护基于网络的分布式系统所必需的，因为日志记录了有关系统行为的丰富信息。近年来，已经提出了许多用于测井分析的工作。但是，它们忽略了日志之间的时间关系。在本文中，我们针对从时间日志数据中挖掘信息模式的问题。我们提出了一种从具有时间规律性的事件序列中发现顺序模式的方法。发现的模式对于工程师了解基于网络的分布式系统的行为很有用。为了解决模式爆炸的众所周知的问题，我们采用最小描述长度（MDL）原理，并在总结模式相邻事件之间的时间关系方面向前迈进了一步。