№1, 2014


Ramiz H. Shikhaliyev

Article devoted to the problem of trends detection in Internet traffic. For solution of this problem we has proposed to use the algorithm of sequential patterns mining. Detecting trends in Internet traffic is very necessary to make the right decisions in the management of computer networks and to allow choosing the basic requirements and possible metrics to monitor them. (pp. 38-46)

Keywords: Internet traffic, detection of Internet traffic trends, sequential patterns mining, itemsets
  • 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.