The KDD process for extracting useful knowledge from volumes of data
Communications of the ACM
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
MPEG-4: multimedia for our time
IEEE Spectrum
A model selection approach for local learning
AI Communications - Special issue on AI research in the Benelux
Distributed Multimedia and QOS: A Survey
IEEE MultiMedia
An Automated Profiling Subsystem for QoS-Aware Services
RTAS '00 Proceedings of the Sixth IEEE Real Time Technology and Applications Symposium (RTAS 2000)
A Dynamic Quality of Service Middleware Agent for Mediating Application Resource Usage
RTSS '98 Proceedings of the IEEE Real-Time Systems Symposium
A Data Analysis Method for Software Performance Prediction
Proceedings of the conference on Design, automation and test in Europe
Differentiated Caching Services; A Control-Theoretical Approach
ICDCS '01 Proceedings of the The 21st International Conference on Distributed Computing Systems
A hierarchical Quality of Service control architecture for configurable multimedia applications
Journal of High Speed Networks
Performance specifications and metrics for adaptive real-time systems
RTSS'10 Proceedings of the 21st IEEE conference on Real-time systems symposium
A control-based middleware framework for quality-of-service adaptations
IEEE Journal on Selected Areas in Communications
| Hi-index | 0.00 |
Quality of Service (QoS) methods aim at trading quality against resource requirements to meet the constraints dictated by the application functionality and the execution platform. QoS is relevant in multimedia tasks since these applications are typically scalable systems. To exploit the scalability property for improving quality, a reliable model of the relation between scalable parameters and quality/resources is required. The traditional QoS approach requires a deep knowledge of the execution platform and a reasonably accurate prediction of the expected configurations. This paper proposes an alternative black-box data analysis approach. The advantage is that it requires no a priori assumptions about the correlation between quality/resources and parameters and it can easily adapt to situations of high complexity, changing platforms and heterogeneous environments. Some preliminary experiments with the QoS modelling of the Visual Texture Coding (VTC) functionality of a MPEG-4 decoder using a local learning technique are presented to support the claim.