FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
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A User-Aware Prefetching Mechanism for Video Streaming
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Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
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User Behaviour Analysis of a Video-On-Demand Service with a Wide Variety of Subjects and Lengths
EUROMICRO '05 Proceedings of the 31st EUROMICRO Conference on Software Engineering and Advanced Applications
Distributed prefetching scheme for random seek support in peer-to-peer streaming applications
Proceedings of the ACM workshop on Advances in peer-to-peer multimedia streaming
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Proceedings of the 1st ACM SIGOPS/EuroSys European Conference on Computer Systems 2006
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Proceedings of the 16th international conference on World Wide Web
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Proceedings of the 2007 conference on Applications, technologies, architectures, and protocols for computer communications
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ICC'09 Proceedings of the 2009 IEEE international conference on Communications
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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.