Analysis of educational media server workloads
NOSSDAV '01 Proceedings of the 11th international workshop on Network and operating systems support for digital audio and video
Characterizing locality, evolution, and life span of accesses in enterprise media server workloads
NOSSDAV '02 Proceedings of the 12th international workshop on Network and operating systems support for digital audio and video
Analyzing client interactivity in streaming media
Proceedings of the 13th international conference on World Wide Web
An analysis of live streaming workloads on the internet
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Understanding user behavior in large-scale video-on-demand systems
Proceedings of the 1st ACM SIGOPS/EuroSys European Conference on Computer Systems 2006
Measurement and analysis of a streaming-media workload
USITS'01 Proceedings of the 3rd conference on USENIX Symposium on Internet Technologies and Systems - Volume 3
Modeling user activities in a large IPTV system
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
Social TV: toward content navigation using social awareness
Proceedings of the 8th international interactive conference on Interactive TV&Video
A Measurement Study of a Large-Scale P2P IPTV System
IEEE Transactions on Multimedia
Social TV: The impact of social awareness on content navigation within IPTV systems
Computers in Entertainment (CIE) - Theoretical and Practical Computer Applications in Entertainment
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In this paper we describe our experiences of developing and monitoring an Internet Television (IPTV) service in order to determine to what extent the system can provide useful hints (or triggers) that are exploitable by Content Distribution Networks (CDNs). Uniquely, our system integrates with social networking services and builds on the social graph to help form an understanding of user behaviour and potentially socially-motivated trends. By combining these attributes we are able to represent a social user journey for each user of the system which we seek to exploit for identifying potential trigger conditions. The paper concludes with an initial analysis of data collected during a five month trial of the service with staff and students of our University.