NDLI: Searching similar segments over textual event sequences

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Searching similar segments over textual event sequences

Content Provider	ACM Digital Library
Author	Tang, Liang Li, Tao Zhu, Shunzhi Chen, Shu-Ching
Abstract	Sequential data is prevalent in many scientific and commercial applications such as bioinformatics, system security and networking. Similarity search has been widely studied for symbolic and time series data in which each data object is a symbol or numeric value. Textual event sequences are sequences of events, where each object is a message describing an event. For example, system logs are typical textual event sequences and each event is a textual message recording internal system operations, statuses, configuration modifications or execution errors. Similar segments of an event sequence reveals similar system behaviors in the past which are helpful for system administrators to diagnose system problems. Existing search indexing for textual data only focus on unordered data. Substring matching methods are able to efficiently find matched segments over a sequence, however, their sequences are single values rather than texts. In this paper, we propose a method, suffix matrix, for efficiently searching similar segments over textual event sequences. It provides an integration of two disparate techniques: locality-sensitive hashing and suffix arrays. This method also supports the k-dissimilar segment search. A k-dissimilar segment is a segment that has at most k dissimilar events to the query sequence. By using random sequence mask proposed in this paper, this method can have a high probability to reach all k-dissimilar segments without increasing much search cost. We conduct experiments on real system log data and the experimental results show that our proposed method outperforms alternative methods using existing techniques.
Starting Page	329
Ending Page	338
Page Count	10
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2505762
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Log event Textual sequence Similarity search
Content Type	Text
Resource Type	Article

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