Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A comparison of some contextual discretization methods
Information Sciences: an International Journal
Introduction to Algorithms
Understanding and Using Context
Personal and Ubiquitous Computing
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
End-to-end differentiation of congestion and wireless losses
IEEE/ACM Transactions on Networking (TON)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Communications of the ACM - Designing for the mobile device
Parameter learning for relational Bayesian networks
Proceedings of the 24th international conference on Machine learning
Learning Bayesian network parameters under order constraints
International Journal of Approximate Reasoning
Bayesian and behavior networks for context-adaptive user interface in a ubiquitous home environment
Expert Systems with Applications: An International Journal
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To support Quality of Service (QoS) management on current Internet working with best effort, we propose a systematic approach for end-to-end QoS qualitative diagnosis and quantitative guarantee. Both QoS metrics and contexts of a service are considered in a comprehensive manner in our approach, which consists of three sequential stages: context discretization, QoS qualitative diagnosis and QoS quantitative guarantee. Based on Fuzzy set, an automatic unwatched discretization algorithm for discretizing continuous numeric-value is brought forth to reshape these QoS metrics and contexts into their discrete forms. For QoS qualitative diagnosis, causal relationships between a QoS metric and its contexts are exploited with the help of K2 Bayesian network (BN) structure learning by treating QoS metrics and contexts as BN nodes. A QoS metric node is qualitatively diagnosed to be causally related to its parent context nodes. An ordering method is proposed to arrange orders for nodes involved in K2 algorithm. To guarantee QoS quantitatively, those causal relationships are next modeled quantitatively by BN parameter learning. BN inference is referred to calculate the marginal on a QoS metric node given its tunable parent context nodes. Then, the QoS metric is guaranteed to a specific value a user demands with certain probability by tuning its causal contexts to suitable values suggested by BN inference, that is, QoS quantitative guarantee is reached by now. Simulations, on a peer-to-peer (P2P) network, about the above three sequential stages are discussed and our approach is validated to be soundable and effective. We also argue that our approach can be reached in a polynomial time complexity in practice.