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LogSig: generating system events from raw textual logs

LogSig: generating system events from raw textual logs LogSig: Generating System Events from Raw Textual Logs Liang Tang School of Computer Science Florida International University 11200 S.W. 8th Street Miami, Florida, 33199 U.S.A Tao Li School of Computer Science Florida International University 11200 S.W. 8th Street Miami, Florida, 33199 U.S.A Chang-Shing Perng IBM T.J. Watson Research Center 19 Skyline Drive Hawthorne, NY, 10532 U.S.A ltang002@cs. u.edu taoli@cs. u.edu perng@us.ibm.com and records system internal operations, such as the starting and stopping of services, detection of network connections, Modern computing systems generate large amounts of log software con guration modi cations, and execution errors. data. System administrators or domain experts utilize the System administrators or domain experts utilize the log data log data to understand and optimize system behaviors. Most to understand and optimize system behaviors. system logs are raw textual and unstructured. One main Most system logs are raw textual and unstructured. There fundamental challenge in automated log analysis is the genare two challenges in analyzing system log data. The rst eration of system events from raw textual logs. Log messages challenge is transforming raw textual logs into system events. are relatively short text messages but may have a large voThe number of distinct events observed can http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

LogSig: generating system events from raw textual logs

Association for Computing Machinery — Oct 24, 2011

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Datasource
Association for Computing Machinery
Copyright
Copyright © 2011 by ACM Inc.
ISBN
978-1-4503-0717-8
doi
10.1145/2063576.2063690
Publisher site
See Article on Publisher Site

Abstract

LogSig: Generating System Events from Raw Textual Logs Liang Tang School of Computer Science Florida International University 11200 S.W. 8th Street Miami, Florida, 33199 U.S.A Tao Li School of Computer Science Florida International University 11200 S.W. 8th Street Miami, Florida, 33199 U.S.A Chang-Shing Perng IBM T.J. Watson Research Center 19 Skyline Drive Hawthorne, NY, 10532 U.S.A ltang002@cs. u.edu taoli@cs. u.edu perng@us.ibm.com and records system internal operations, such as the starting and stopping of services, detection of network connections, Modern computing systems generate large amounts of log software con guration modi cations, and execution errors. data. System administrators or domain experts utilize the System administrators or domain experts utilize the log data log data to understand and optimize system behaviors. Most to understand and optimize system behaviors. system logs are raw textual and unstructured. One main Most system logs are raw textual and unstructured. There fundamental challenge in automated log analysis is the genare two challenges in analyzing system log data. The rst eration of system events from raw textual logs. Log messages challenge is transforming raw textual logs into system events. are relatively short text messages but may have a large voThe number of distinct events observed can

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