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Finding Relevant Dimensions in Application Service Management Control

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Intelligent Systems for Science and Information

Part of the book series: Studies in Computational Intelligence ((SCI,volume 542))

Abstract

The increased interest in autonomous control in Application Service Management environments has driven a demand for analysis of multivariate datasets in this area. This paper proposes a feature selection method using metrics time series analysis. The method exploits four metrics called Similarity, Dependency, Consequence, Interference which are combined in order to perform a multivariate evaluation. This allows more efficient search for similarities in the time-series, selection of most relevant dimensions, and easier control in the reduced space, which would ultimately reduce maintenance effort. This is further used to create causal models of the controlled system, significantly simplifying evaluation of defined elements utilization dependencies. We show that methods based on these metrics can be applied in service control practice under several scenarios.

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References

  1. Haines, S.: Pro Java EE 5 performance management and optimization. Apress (2006)

    Google Scholar 

  2. Sydor, M.J.: APM Best Practices: Realizing Application Performance Management. Apress (2010)

    Google Scholar 

  3. Grinshpan, L.: Workload characterization and transaction profiling. In: Solving Enterprise Applications Performance Puzzles: Queuing Models to the Rescue, pp. 57–94 (2012)

    Google Scholar 

  4. Sikora, T., Magoulas, G.D.: Neural adaptive control in application service management environment. In: Jayne, C., Yue, S., Iliadis, L. (eds.) EANN 2012. CCIS, vol. 311, pp. 223–233. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  5. Pérez, P.A., Antonio, S., Sala, A.: Multivariable control systems: an engineering approach. Springer (2004)

    Google Scholar 

  6. Pearl, J.: Causality: models, reasoning and inference, vol. 29. Cambridge Univ. Press (2000)

    Google Scholar 

  7. Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. The Journal of Machine Learning Research 3, 1157–1182 (2003)

    MATH  Google Scholar 

  8. Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., Keogh, E.: Querying and mining of time series data: experimental comparison of representations and distance measures. Proceedings of the VLDB Endowment 1(2), 1542–1552 (2008)

    Google Scholar 

  9. Box, G.E., Luceno, A., del Carmen Paniagua-Quiñones, M.: Statistical control by monitoring and adjustment, vol. 898. Wiley (2011)

    Google Scholar 

  10. Box, G.E., Jenkins, G.M., Reinsel, G.C.: Time series analysis: forecasting and control, vol. 734. Wiley (2011)

    Google Scholar 

  11. Holland, P.W.: Statistics and causal inference. Journal of the American statistical Association 81(396), 945–960 (1986)

    Article  MATH  MathSciNet  Google Scholar 

  12. Wright, S.: The method of path coefficients. The Annals of Mathematical Statistics 5(3), 161–215 (1934)

    Article  Google Scholar 

  13. Wright, S.: Correlation and causation. Journal of Agricultural Research 20(7), 557–585 (1921)

    Google Scholar 

  14. Granger, C.W.: Investigating causal relations by econometric models and cross-spectral methods. Econometrica: Journal of the Econometric Society, 424–438 (1969)

    Google Scholar 

  15. Chen, Y., Rangarajan, G., Feng, J., Ding, M.: Analyzing multiple nonlinear time series with extended granger causality. Physics Letters A 324(1), 26–35 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  16. Eichler, M.: Granger causality and path diagrams for multivariate time series. Journal of Econometrics 137(2), 334–353 (2007)

    Article  MathSciNet  Google Scholar 

  17. Guyon, I., Elisseeff, A., Aliferis, C.: Causal feature selection (2007)

    Google Scholar 

  18. Ihler, A., Hutchins, J., Smyth, P.: Adaptive event detection with time-varying poisson processes. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 207–216. ACM (2006)

    Google Scholar 

  19. Hunter, J., McIntosh, N.: Knowledge-based event detection in complex time series data. In: Horn, W., Shahar, Y., Lindberg, G., Andreassen, S., Wyatt, J.C. (eds.) AIMDM 1999. LNCS (LNAI), vol. 1620, pp. 271–280. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  20. Geng, X., Liu, T.-Y., Qin, T., Li, H.: Feature selection for ranking. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 407–414. ACM (2007)

    Google Scholar 

  21. Wei, H.-L., Billings, S.A.: Feature subset selection and ranking for data dimensionality reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(1), 162–166 (2007)

    Article  Google Scholar 

  22. Seppala, C., Harris, T., Bacon, D.: Time series methods for dynamic analysis of multiple controlled variables. Journal of Process Control 12(2), 257–276 (2002)

    Article  Google Scholar 

  23. Berndt, D., Clifford, J.: Using dynamic time warping to find patterns in time series. In: KDD Workshop, Seattle, WA, vol. 10(16), pp. 359–370 (1994)

    Google Scholar 

  24. Keogh, E.: Exact indexing of dynamic time warping. In: Proceedings of the 28th International Conference on Very Large Data Bases. VLDB Endowment, pp. 406–417 (2002)

    Google Scholar 

  25. Pavlidis, T., Horowitz, S.L.: Segmentation of plane curves. IEEE Transactions on Computers 100(8), 860–870 (1974)

    Article  MathSciNet  Google Scholar 

  26. Keogh, E.J., Pazzani, M.J.: Scaling up dynamic time warping for datamining applications. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 285–289. ACM (2000)

    Google Scholar 

  27. Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Locally adaptive dimensionality reduction for indexing large time series databases. ACM SIGMOD Record 30(2), 151–162 (2001)

    Article  Google Scholar 

  28. Latecki, L.J., Megalooikonomou, V., Wang, Q., Lakämper, R., Ratanamahatana, C.A., Keogh, E.J.: Elastic partial matching of time series. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 577–584. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  29. Hall, M.A.: Correlation-based feature selection for machine learning. Ph.D. dissertation, The University of Waikato (1999)

    Google Scholar 

  30. Keogh, E., Chakrabarti, K., Pazzani, M., Mehrotra, S.: Dimensionality reduction for fast similarity search in large time series databases. Knowledge and Information Systems 3(3), 263–286 (2001)

    Article  MATH  Google Scholar 

  31. Kantz, H., Schreiber, T.: Nonlinear time series analysis, vol. 7. Cambridge University Press (2003)

    Google Scholar 

  32. Gruschke, B., et al.: Integrated event management: Event correlation using dependency graphs. In: Proceedings of the 9th IFIP/IEEE International Workshop on Distributed Systems: Operations & Management (DSOM 1998), pp. 130–141 (1998)

    Google Scholar 

  33. Keller, A., Kar, G.: Determining service dependencies in distributed systems. In: IEEE International Conference on Communications, ICC 2001, vol. 7, pp. 2084–2088. IEEE (2001)

    Google Scholar 

  34. Agarwal, M.K., Sachindran, N., Gupta, M., Mann, V.: Fast extraction of adaptive change point based patterns for problem resolution in enterprise systems. In: Large Scale Management of Distributed Systems, pp. 161–172. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  35. Jiang, G., Chen, H., Yoshihira, K., Saxena, A.: Ranking the importance of alerts for problem determination in large computer systems. Cluster Computing 14(3), 213–227 (2011)

    Article  Google Scholar 

  36. Kıcıman, E.: Using statistical monitoring to detect failures in internet services. Ph.D. dissertation, Stanford University (2005)

    Google Scholar 

  37. Pyle, D.: Data preparation for data mining, vol. 1. Morgan Kaufmann (1999)

    Google Scholar 

  38. Reinsch, C.H.: Smoothing by spline functions. Numerische Mathematik 10(3), 177–183 (1967)

    Article  MATH  MathSciNet  Google Scholar 

  39. Wang, Y.: Smoothing spline models with correlated random errors. Journal of the American Statistical Association 93(441), 341–348 (1998)

    Article  MATH  Google Scholar 

  40. Comon, P., Jutten, C.: Handbook of Blind Source Separation: Independent component analysis and applications. Academic Press (2010)

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Information Systems, Birkbeck, University of London, Malet Street, London, WC1E 7HX, UK

    Tomasz D. Sikora & George D. Magoulas

Authors
  1. Tomasz D. Sikora
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  2. George D. Magoulas
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Correspondence to Tomasz D. Sikora .

Editor information

Editors and Affiliations

  1. School of Computer Science and Informatics, De Montfort University, The Gateway, Leicester, LE1 9BH, United Kingdom

    Liming Chen

  2. The Science and Information Organization, New York, USA

    Supriya Kapoor

  3. The Science and Information Organization, New York, USA

    Rahul Bhatia

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Sikora, T.D., Magoulas, G.D. (2014). Finding Relevant Dimensions in Application Service Management Control. In: Chen, L., Kapoor, S., Bhatia, R. (eds) Intelligent Systems for Science and Information. Studies in Computational Intelligence, vol 542. Springer, Cham. https://doi.org/10.1007/978-3-319-04702-7_19

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  • DOI: https://doi.org/10.1007/978-3-319-04702-7_19

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-04701-0

  • Online ISBN: 978-3-319-04702-7

  • eBook Packages: EngineeringEngineering (R0)

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