Knowledge Acquisition for Automation in IT Infrastructure Support

  • Sandeep Chougule
  • Trupti Dhat
  • Veena Deshmukh
  • Rahul Kelkar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8891)

Abstract

In todays IT-driven world, the IT Infrastructure Support (ITIS) unit aims for effective and efficient management of IT infrastructure of large and modern organizations. Automatic issue resolution is crucial for operational efficiency and agility of ITIS. For manually creating such automatic issue resolution processes, a Subject Matter Expert (SME) is required. Our focus is on acquiring SME knowledge for automation. Additionally, the number of distinct issues is large and resolution of issue instances requires repetitive application of resolver knowledge. Operational logs generated from the resolution process of issues, is resolver knowledge available in tangible form.

We identify functional blocks from the operational logs, as potential standard operators, which the SME will validate and approve. We algorithmically consolidate all the steps the resolvers have performed historically during the resolution process for a particular issue, and present to the SME a graphical view of the consolidation for his assessment and approval. We transform the graphical view into a set of rules along with the associated standard operators and finally ensemble them into a parametrized service operation in tool agnostic language. For an ITIS automation system, it is transformed into a configuration file of a targeted orchestrator tool. Bash and powershell script transformations of service operations are executed by resolvers manually or via an automation web portal.

References

  1. 1.
    Inokuchi, A., Washio, T., Motoda, H.: An apriori-based algorithm for mining frequent substructures from graph data. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 13–23. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  2. 2.
    Robles-Kelly, A., Hancock, E.R.: Graph edit distance from spectral seriation. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(3), 365–378 (2005)CrossRefGoogle Scholar
  3. 3.
    Srikant, R., Agrawal, R.: Mining sequential patterns: Generalizations and performance improvements. In: Apers, P.M.G., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 3–17. Springer, Heidelberg (1996)Google Scholar
  4. 4.
    Wang, J.T.L., Chirn, G.W., Marr, T.G., Shapiro, B., Shasha, D., Zhang, K.: Combinatorial pattern discovery for scientific data: Some preliminary results. In: Proceedings of the 1994 ACM SIGMOD International Conference on Management of Data, SIGMOD 1994, pp. 115–125. ACM, New York (1994), http://doi.acm.org/10.1145/191839.191863 CrossRefGoogle Scholar
  5. 5.
    Wang, J.T.-L., Chirn, G.-W., Marr, T.G., Shapiro, B., Shasha, D., Zhang, K.: Combinatorial pattern discovery for scientific data: Some preliminary results. SIGMOD Rec. 23(2), 115–125 (1994), http://doi.acm.org/10.1145/191843.191863 CrossRefGoogle Scholar
  6. 6.
    Yang, J., Wang, W., Yu, P.S., Han, J.: Mining long sequential patterns in a noisy environment. In: Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data, SIGMOD 2002, pp. 406–417. ACM, New York (2002), http://doi.acm.org/10.1145/564691.564738 CrossRefGoogle Scholar
  7. 7.
    Yu, H., Hancock, E.: String kernels for matching seriated graphs. In: Proceedings of the 18th International Conference on Pattern Recognition, vol. 1, pp. 224–228 (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Sandeep Chougule
    • 1
  • Trupti Dhat
    • 1
  • Veena Deshmukh
    • 1
  • Rahul Kelkar
    • 1
  1. 1.Tata Research Development and Design CentrePuneIndia

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