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FLP: a feature-based method for log parsing

FLP: a feature-based method for log parsing

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A feature-based log parsing method is presented for extracting log events from unstructured free-text logs. It is a data-driven log analytic solution with no training data needed and suitable for various types of log parsing tasks. Experiments show that the proposed method can achieve higher accuracy and lower time complexity in large-scale log data than existing log parsing methods.

References

    1. 1)
      • 1. Lang, D.: ‘Using SEC’, USENIX;login: Magazine, 2013, vol. 38.
    2. 2)
      • 2. Zhu, J., He, P., Fu, Q., et al: ‘Learning to log: helping developers make informed logging decisions’. IEEE/ACM, IEEE Int. Conf. Software Engineering, Firenze, Italy, May 2015, pp. 415425.
    3. 3)
      • 3. Vaarandi, R.: ‘A data clustering algorithm for mining patterns from event logs’. IEEE Workshop on Ip Operations & Management, Kansas City, MO, USA, October 2003, pp. 119126.
    4. 4)
      • 4. Fu, Q., Lou, J.G., Wang, Y., et al: ‘Execution anomaly detection in distributed systems through unstructured log analysis’. Ninth IEEE Int. Conf. Data Mining IEEE Computer Society, Miami, FL, USA, December 2009, pp. 149158.
    5. 5)
      • 5. Tang, L., Li, T., Perng, C.S.: ‘Logsig: generating system events from raw textual logs’. ACM, 2011, pp. 785794.
    6. 6)
      • 6. Makanju, A., Zincir-Heywood, A.N., Milios, E.E.: ‘A lightweight algorithm for message type extraction in system application logs’, IEEE Educational Activities Department, 2012.
    7. 7)
      • 7. He, P., Zhu, J., He, S., et al: ‘An evaluation study on log parsing and its use in log mining’. IEEE/IFIP Int. Conf. Dependable Systems and Networks, Toulouse, France, June 2016, pp. 654661.
    8. 8)
      • 8. Du, M., Li, F.: ‘Spell: streaming parsing of system event logs’. Int. Conf. Data Mining, Barcelona, Spain, December 2017, pp. 859864.
    9. 9)
      • 9. He, P., Zhu, J., Zheng, Z., et al: ‘Drain: an online log parsing approach with fixed depth tree’. Int. Conf. Web Services, Honolulu, HI, USA, June 2017, pp. 3340.
    10. 10)
      • 10. L. A. N. S. LLC. Operational data to support and enable computer science research. Available at: http://institutes.lanl.gov/data/fdata.
    11. 11)
      • 11. Evaluation of clustering. Available at: http://nlp.stanford.edu/IR-book/html/htmledition/evaluation-of-clustering-1.html.
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