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  1. Proceedings of the first SIGMM workshop on Social media (WSM '09)
  2. Accelerating YouTube with video correlation
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Influence of multimedia in emerging social web systems
Tweet the debates: understanding community annotation of uncollected sources
Effective semantic classification of consumer events for automatic content management
Accelerating YouTube with video correlation
Motivating contributors in social media networks
Event driven summarization for web videos
Multimodal video copy detection applied to social media
Image tag clarity: in search of visual-representative tags for social images
The role of tags and image aesthetics in social image search
From usage to annotation: analysis of personal photo albums for semantic photo understanding
Social reader: following social networks in the wilds of the blogosphere
Implicit emotional tagging of multimedia using EEG signals and brain computer interface
VisuaPedia: a social media based visual encyclopedia

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Accelerating YouTube with video correlation

Content Provider ACM Digital Library
Author Wang, Haiyang Cheng, Xu Liu, Jiangchuan
Abstract In this paper, using long-term data traces, we present an in-depth measurement study on the characteristics of YouTube, the most successful site providing a new generation of short video sharing service. We find that YouTube videos have noticeable differences compared with traditional videos, making it difficult to use conventional strategies, such as peer-to-peer, to reduce the server workload. However, the video correlation presented in YouTube opens new opportunities. We design a novel peer-to-peer short video sharing system based on video correlation, in which peers are responsible for re-distributing the videos that they have cached. We address a series of key design issues to realize the system, including a novel architecture design, an efficient indexing scheme and a source rate allocation mechanism. We perform extensive simulations, which show that the system greatly reduces the server workload and improves the playback quality.
Starting Page 49
Ending Page 56
Page Count 8
File Format PDF
ISBN 9781605587592
DOI 10.1145/1631144.1631156
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2009-10-23
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Video on demand Peer-to-peer Youtube Social network
Content Type Text
Resource Type Article
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