Recently, a large amount of data is stored in databases through the advance of computer and network environments. To acquire knowledge from the databases is important for analyses of the present condition of the systems and for predictions of coming incidents. The log file is one of the databases stored automatically in computer systems. Unexpected incidents such as system troubles as well as the histories of daily service programs' actions are recorded in the log files. System administrators have to check the messages in the log files in order to analyze the present condition of the systems. However, the descriptions of the messages are written in various formats according to the kinds of service programs and application software. It may be difficult to understand the meaning of the messages without the manuals or specifications. Moreover, the log files become enormous, and important messages are liable to mingle with a lot of insignificant messages. Therefore, checking the log files is a troublesome task for administrators.
Log monitoring tools such as SWATCH [1], in which regular expressions for representing problematic phrases are used for pattern matching, are effective for detecting well-known typical error messages. However, various programs running in the systems may be open source software or software companies’ products, and they may have been newly developed or upgraded recently. Therefore, it is impossible to detect all the problematic messages by the predefined rules. In addition, in order to cope with illegal use by hackers, it is important to detect unusual behavior such as the start of the unsupposed service program, even if the message does not correspond to the error message. To realize this system, the error-detection rules depending on the environment of the systems should be acquired adaptively by means of evolution or learning.
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References
SWATCH: The Simple WATCHer of Logfiles. (2007) http://swatch.sourceforge.net/
J.R. Koza (1992) Genetic Programming – On the Programming of Computers by Means of Natural Selection. The MIT Press
C.C. Bojarczuk, H.S. Lopes, A.A. Freitas (2000) Genetic programming for knowledge discovery in chest-pain diagnosis. IEEE Engineering in Medicine and Biology. Vol. 19, No. 4, pp. 38–44
C.C. Bojarczuk, H.S. Lopes, A.A. Freitas (2003) An innovative application of a constrained-syntax genetic programming system to the problem of predicting survival of patients. In: Proceedings of Euro GP 2003. pp. 11–21
L. Hirsch, M. Saeedi, R. Hirsch (2005) Evolving rules for document classification. In: Proceedings of Euro GP 2005. pp. 85–95
A. Hara, T. Nagao (1999) Emergence of cooperative behavior using ADG; Automatically defined groups. In: Proceedings of the 1999 Genetic and Evolutionary Computation Conference. pp. 1039–1046
A. Hara, T. Nagao (2002) Construction and analysis of stock market model using ADG; Automatically defined groups. International Journal of Computational Intelligence and Applications (IJCIA). Vol. 2, No. 4, pp. 433–446
A. Hara, T. Ichimura, K. Yoshida (2005) Discovering multiple diagnostic rules from coronary heart disease database using automatically defined groups. Journal of Intelligent Manufacturing. Vol. 16, No. 6, pp. 645–661
A. Hara, T. Ichimura, T. Takahama, Y. Isomichi (2004) Discovery of cluster structure and the clustering rules from medical database using ADG; Automatically defined groups. In: T. Ichimura and K. Yoshida (eds) Knowledge-Based Intelligent Systems for Healthcare. pp. 51–86, CRC Press
T. Ichimura, S. Oeda, M. Suka, A. Hara, K.J. Mackin, K. Yoshida (2005) Knowledge discovery and data mining in medicine. In: N. Pal and L.C. Jain (eds) Advanced Techniques in Knowledge Discovery and Data Mining. pp. 177–210, Springer
A. Hara, T. Ichimura, T. Takahama, Y. Isomichi (2005) Extraction of risk factors by multi-agent voting model using automatically defined groups. In: Proceedings of the Ninth Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES’2005). Vol. 3, pp. 1218–1224
A. Hara, T. Ichimura, T. Takahama, Y. Isomichi (2003) Extraction of rules by heterogeneous agents using automatically defined groups. In: Proceedings of the Seventh Conference on Knowledge-Based Intelligent Information and Engineering Systems (KES’2003). Vol. 2, pp. 1405–1411
S. Luke, L. Spector (1996) Evolving teamwork and coordination with genetic programming. In: Genetic Programming 1996: Proceedings of the First Annual Conference. pp. 150–156
H. Iba (1996) Emergent cooperation for multiple agents using genetic programming. In: Parallel Problem Solving from Nature IV. Proceedings of the International Conference on Evolutionary Computation. pp. 32–41
H. Iba (1997) Multiple-agent learning for a robot navigation task by genetic programming. In: Genetic Programming 1997: Proceedings of the Second Annual Conference. pp. 195–200
Y. Kurosawa, A. Hara, T. Ichimura, Y. Kawano (2006) Extraction of error detection rules without supervised information from log files using automatically defined groups. In: Proceedings of The 2006 IEEE International Conference on System, Man and Cybernetics. pp. 5314–5319
T. Haynes, R. Wainwright, S. Sen, D. Schoenefeld (1995) Strongly typed genetic programming in evolving cooperation strategies. In: Genetic Algorithms: Proceedings of the Sixth International Conference (ICGA95). pp. 271–278
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Hara, A., Kurosawa, Y., Ichimura, T. (2008). Automatically Defined Groups for Knowledge Acquisition from Computer Logs and Its Extension for Adaptive Agent Size. In: Castillo, O., Xu, L., Ao, SI. (eds) Trends in Intelligent Systems and Computer Engineering. Lecture Notes in Electrical Engineering, vol 6. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-74935-8_2
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