№1, 2014
ОБ ОДНОМ МЕТОДЕ ОБНАРУЖЕНИЯ ТРЕНДОВ В ИНТЕРНЕТ-ТРАФИКЕ
Статья посвящена задаче обнаружения трендов в интернет-трафике. Для этого предлагается использовать алгоритм обнаружения последовательных шаблонов. Обнаружение трендов в интернет-трафике необходимо для принятия правильных решений при управлении компьютерных сетей и позволит выбрать базовые требования и возможные метрики для их мониторинга. (стр. 38-46)
Ключевые слова: интернет-трафик, обнаружение трендов в интернет-трафике, обнаружение последовательных шаблонов, набор часто встречаемых элементов
Литература
- Zeng B. D., Zhang W. Li, Zhang M., Hong Q. An adaptive sampling methodology for internet traffic data measurement / Proceedings of the International Conference on Communication Software and Networks, 2009, Feb. 27–28, pp.215–218.
- Agrawal R., Srikant R. Mining Sequential Patterns // Journal Intelligent Systems, 1997, vol.9, no.1, pp.33–56.
- Agrawal R., Srikant R. Mining sequential patterns: Generalizations and performance improvements / Proceedings of the 5th International Conference on Extending Database Technology, 1996, pp.1–17.
- Han J., Kamber M., Data mining: concepts and techniques. Morgan Kaufmann, 2006.
- Lamparter O. and Stauffer B., A network traffic measurement tool / Proceedings of the 10th International Conference on Telecommunications, 2003, Feb. 23-Mar., vol.1, pp.1078–1083.
- Zhanh L., Tang J. Characterization and performance study of IP traffic in WDM networks // Computer communications, 2001, no.24, pp.1702–1713.
- Paxson V. Empirically derived analytic models of wide-area TCP connections / IEEE / ACM Trans. Netw., 1994, vol.2, no.4, pp.316–336.
- Paxson V. and Floyd S., Wide area traffic: the failure of Poisson modeling / IEEE/ACM Trans. Netw., 1995, vol.3, no.3, pp.226–244.
- Karagiannis T., Papagiannaki K., Faloutsos M. BLINC: multilevel traffic classification in the dark / Proceedings of the Conference on Applications, Technologies, Architectures, and Protocols for Pomputer Communications, 2005, New York, pp.229–240.
- Zaki M. J. Spade: An efficient algorithm for mining frequent sequences // Machine Learning 2001, vol.42, no.1–2, pp.31–60.
- Pei J., Han J., Mortazavi-Asl B., etc. Mining sequential patterns by pattern-growth: The prefixspan approach / IEEE Transactions on Knowledge and Data Engineering 2004, vol.16, no.11, pp.1424–1440.
- Zaki M.J. Scalable data mining for rules, Technical Report Ph.D. Dissertation, University of Rochester, New York, 1998.
- Ming-Yen Lin, Suh-Yin Interactive Sequence Discovery by Incremental Mining // An International Journal of Information Sciences-Informatics and Computer Science, 2004, vol.165, no.3–4, pp.187–205.
- Mabroukeh RN., Ezeife C. I. A taxonomy of sequential pattern mining algorithms // Journal ACM Computing Surveys, 2010, vol.43, no. 3.
- Chandra V. Shekhar Rao, Sammula P. Survey on Sequential Pattern Mining Algorithms / International Journal of Computer Applications, 2013, vol.76, no.12, pp.24–31.
- Parikh M., Chaudhari B. and Chand C., A Comparative Study of Sequential Pattern Mining Algorithms // International Journal of Application or Innovation in Engineering and Management, 2013, vol.2, no.2, pp.103–109.
- Agrawal R. and Srikant R., Mining sequential patterns. Research Report RJ9910, IBM Almaden Research Center, San Jose, California, October 1994.
- Pei J., Han J., Mortazavi-Asl B., etc. PrefixSpan: mining sequential patterns efficiently by prefix projected pattern growth / Proceedings of the 17th International Conference on Data Engineering, 2001, pp.215–226.