Quality is in the eye of the beholder: meeting users' requirements for Internet quality of service
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Using analytic QP and sparseness to speed training of support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
NIST Net: a Linux-based network emulation tool
ACM SIGCOMM Computer Communication Review
Web QoE Evaluation in Multi-agent Networks: Validation of ITU-T G.1030
ICAS '09 Proceedings of the 2009 Fifth International Conference on Autonomic and Autonomous Systems
On dominant characteristics of residential broadband internet traffic
Proceedings of the 9th ACM SIGCOMM conference on Internet measurement conference
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
IEEE Transactions on Audio, Speech, and Language Processing
From packets to people: quality of experience as a new measurement challenge
DataTraffic Monitoring and Analysis
Internet video delivery in youtube: from traffic measurements to quality of experience
DataTraffic Monitoring and Analysis
| Hi-index | 0.00 |
Quality of Experience (QoE) has gained enormous attention during the recent years. So far, most of the existing QoE research has focused on audio and video streaming applications, although HTTP traffic carries the majority of traffic in the residential broadband Internet. However, existing QoE models for this domain do not consider temporal dynamics or historical experiences of the user's satisfaction while consuming a certain service. This psychological influence factor of past experience is referred to as the memory effect. The first contribution of this paper is the identification of the memory effect as a key influence factor for Web QoE modeling based on subjective user studies. As second contribution, three different QoE models are proposed which consider the implications of the memory effect and imply the required extensions of the basic models. The proposed Web QoE models are described with a) support vector machines, b) iterative exponential regressions, and c) two-dimensional hidden Markov models.