Probabilistic seeking prediction in P2P VoD systems

Weiwei Wang, Tianyin Xu, Yang Gao, Sanglu Lu

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In P2P VoD streaming systems, user behavior modeling is critical to help optimise user experience as well as system throughput. However, it still remains a challenging task due to the dynamic characteristics of user viewing behavior. In this paper, we consider the problem of user seeking prediction which is to predict the user's next seeking position so that the system can proactively make response.We present a novel method for solving this problem. In our method, frequent sequential patterns mining is first performed to extract abstract states which are not overlapped and cover the whole video file altogether. After mapping the raw training dataset to state transitions according to the abstract states, we use a simpel probabilistic contingency table to build the prediction model. We design an experiment on the synthetic P2P VoD dataset. The results demonstrate the effectiveness of our method.

Original languageEnglish (US)
Title of host publicationAI 2009
Subtitle of host publicationAdvances in Artificial Intelligence - 22nd Australasian Joint Conference, Proceedings
Number of pages10
StatePublished - 2009
Externally publishedYes
Event22nd Australasian Joint Conference on Artificial Intelligence, AI 2009 - Melbourne, VIC, Australia
Duration: Dec 1 2009Dec 1 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5866 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other22nd Australasian Joint Conference on Artificial Intelligence, AI 2009
CityMelbourne, VIC


  • Contingency table
  • P2P VoD systems
  • PrefixSpan
  • State abstraction
  • User behavior modeling
  • User seeking prediction

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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