Event mining is a useful way to understand computer system behaviors. The focus of recent works on event mining has been shifted to event summarization from discovering frequent patterns. Event summarization seeks to provide a comprehensible explanation of the event sequence on certain aspects. Previous methods have several limitations such as high time complexity, a low precision especially with the presence of noise and phase shifts, and providing a summary which is difficult for a human to understand. In this paper, we propose an integrated event summarization approach towards the understanding of the chaotic temporal data. Our approach focuses on two kinds of temporal relationships, the periodic pattern and the correlation pattern, hidden in event sequences of at most two event types. For the periodic patterns, we propose an event periodicity detection algorithm to discover them directly.For the correlation patterns, we make a simple statistical test based on low order statistics to check temporal dependency of events of two types and to eliminate event correlation candidate space dramatically, and then apply inter-arrival histograms to summarize an event sequence and capture the correlation patterns. In order to balance between accuracy and brevity, the minimum description length principle is used to guide the summarization process. Further, the event relationship network is built to describe discovered patterns. We conduct several groups of experiments on synthetic and real data. Experimental results show our approach is capable of producing usable event summarization, robust to noises, and scalable. The average compression ratio of event sequences reaches 99.7%.
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