BazEkon - The Main Library of the Cracow University of Economics

BazEkon home page

Main menu

Author
Cichowicz Tomasz (Politechnika Poznańska), Frankiewicz Michał (Politechnika Poznańska), Rytwiński Filip (Politechnika Poznańska), Wasilewski Jacek (Politechnika Poznańska), Zakrzewicz Maciej (Politechnika Poznańska)
Title
Odkrywanie anomalii w szeregach czasowych pochodzących z monitoringu systemów teleinformatycznych
Anomaly Detection in Time Series for System Monitoring
Source
Zeszyty Naukowe Wyższej Szkoły Bankowej w Poznaniu, 2012, nr 40, s. 115-130, bibliogr. 28 poz.
The Poznan School of Banking Research Journal
Issue title
Information and communication technology w gospodarce opartej na wiedzy. Wybrane aspekty teoretyczne i aplikacyjne ; Information and Communication Technology in Knowledge Economy Selected Theoretical and Application Aspects
Keyword
Analiza szeregów czasowych, Systemy teleinformatyczne
Time-series analysis, Telecommunication systems
Note
streszcz., summ.
Abstract
Zautomatyzowana analiza szeregów czasowych pochodzących z monitoringu systemów teleinformatycznych jest odpowiedzią na rosnącą złożoność topologiczną i techniczną współczesnych systemów. Jednym z trudniejszych zagadnień z zakresu analizy szeregów czasowych jest wykrywanie anomalii, sygnalizujących awarię lub niewłaściwe użycie systemu teleinformatycznego. W artykule omówiono kontekst wykrywania anomalii w szeregach czasowych pochodzących z monitoringu systemów teleinformatycznych, dokonano przeglądu dotychczasowych metod i algorytmów, zaproponowano dwie nowe metody wykrywania anomalii oraz zaprezentowano wyniki złożonych badań eksperymentalnych.(abstrakt oryginalny)

Automated analysis of time series describing performance indicators is a common requirement for efficient monitoring of large, complex, distributed IT systems. One of the most challenging tasks in time series analysis is anomaly detection as the anomalies may indicate failures or misuse of an IT system. In this paper we focus on anomaly detection methods in time series describing key performance indicators of an IT system. We study the existing methods and propose two new approaches. Our results have been verified in a series of experiments.(original abstract)
Accessibility
The Main Library of the Cracow University of Economics
The Library of Warsaw School of Economics
The Library of University of Economics in Katowice
The Main Library of Poznań University of Economics and Business
Full text
Show
Bibliography
Show
  1. Averaging and exponential smoothing models, www.duke.edu/~rnau/411avg.htm [01.2012].
  2. Barford P., Kline J., Plonka D., Ron A., A signal analysis of network trafic anomalies, w: IMW'02 Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurement, ACM, New York 2002, s. 71-82.
  3. Bertsekas D., Tsitsiklis J., Probabilistic systems analysis and applied probability, http://ocw.mit. edu/courses/electrical-engineering-and-computer-science/6-041-probabilistic-systems-analysis-and-applied-probability-fall-2010/lecture-notes [01.2012].
  4. Bollinger J., Bollinger on Bollinger bands, McGraw-Hill, 2001.
  5. Cepstral smoothing, https://ccrma.stanford.edu/~jos/SpecEnv/Cepstral_Smoothing.html [01.2012].
  6. Chandola V., Banerjee A., Kumar V., Anomaly detection. A survey. ACM, "Comput. Surv.", lipiec 2009.
  7. Durbin J., Efficient estimation of parameters in moving-average models, "Biometrika" 1959, nr 3.
  8. Encyklopedia analizy technicznej, www.wdsoftware.com/pl/encyklopedia-at/index.html [01.2012].
  9. Factor analysis, www.psych.cornell.edu/Darlington/factor.htm [23.01.2012].
  10. Fawcett T., An introduction to roc analysis, "Pattern Recogn. Lett." 2006, nr 27, s. 861-874.
  11. Gao J., Hu G., Yao X., Chang R.K.C., Anomaly detection of network traffic based on wavelet packet, APCC '06. Asia-Pacific Conference on Communications, 2006.
  12. Generating mechanical forecasts from statistical models, www.mrp3.com/fcst_models.html [01.2012].
  13. Krzanowski W.J., Principles of multivariate analysis: a user's perspective, "Oxford statistical science series", Oxford University Press, Oxford 2000.
  14. Kumar N., Lolla N., Keogh E., Lonardi S., Ratanamahatana Ch.A., Time-series bitmaps: a practical visualization tool for working with large time series databases, SIAM 2005 Data Mining Conference, SIAM, 2005, s. 531-535.
  15. Lin J., Keogh E., Lonardi S., Chiu B., A symbolic representation of time series, with implications for streaming algorithms, Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, ACM Press, 2003.
  16. Lo A.W., Mamaysky H., Wang J., Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation, "The Journal of Finance" 2000, nr 55(4), s. 1705-1770.
  17. Murphy J.J., Technical analysis of the financial markets, "Pennsylvania Dental Journal" 1999, nr 77(2).
  18. Naiwny klasyfikator Bayesa, www.statsoft.com.pl/textbook/stnaiveb.html [01.2012].
  19. Ng A., Machine learning, www.ml-class.org/course/auth/welcome [01.2012].
  20. OpenForecastAPI, http://openforecast.sourceforge.net/docs [01.2012].
  21. Smith III J.O., MUS421/EE367B applications lecture b: Cross synthesis using cepstral smoothing or linear prediction for spectral envelopes, https://ccrma.stanford.edu/~jos/SpecEnv/SpecEnv. pdf [01.2012].
  22. Stefanowski J., Analiza szeregów czasowych, www.cs.put.poznan.pl/jstefanowski/aed/TPtimeseries.pdf [01.2012].
  23. Thrun S., Norvig P., Online introduction to artificial intelligence, www.ai-class.com/course/topic/6 [01.2012].
  24. Triple exponential smoothing, www.itl.nist.gov/div898/handbook/pmc/section4/pmc435.htm [01. 2012].
  25. Wei L., Kumar N., Lolla V., Keogh E., Lonardi S., Ratanamahatana Ch.A., Assumption-free anomaly detection in time series, Proceedings of the 17th International Conference on Scientic and Statistical Database Management 2005, s. 237-242.
  26. Wong W.-K., Moore A., Cooper G., Wagner M., Bayesian network anomaly pattern detection for disease outbreaks, Proceedings of the Twentieth International Conference on Machine Learning, Menlo Park, California, lipiec 2003, AAAI Press, s. 808-815.
  27. Wong W.-K., Moore A., Cooper G., Wagner M., What's Strange About Recent Events. "Journal of Urban Health", czerwiec 2003, Supplement 1.
  28. Wong W.-K., Moore A., Cooper G., Wagner M., What's Strange About Recent Events (WSARE): An algorithm for the early detection of disease outbreaks, "Journal of Machine Learning Research" 2005, nr 6.
Cited by
Show
ISSN
1426-9724
Language
pol
Share on Facebook Share on Twitter Share on Google+ Share on Pinterest Share on LinkedIn Wyślij znajomemu