An optimized service selection model based on dynamic trust

As the development of WoT(Web of Things), a large mount of services emerge. How to make full use of these services is a hot research area nowadays. This paper focuses on making these services more valuable in WoT Smart Home scenario. In this paper, we propose a model called service selection model based on rule and statistics into WoT Smart Home scenario. It is a combination of rule model and statistics model. It not only automatizes the service selection process by adopting statistics algorithm, but also dramatically improves the accuracy of this process by combining rule and statistics. Further more, it saves efforts of the annotation of service rules greatly. We carry out some experiments to verify the efficiency of this model by comparing with rule model and statistics respectively. The results show that the combination model can efficiently and precisely select services.

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Composite service selection model based on two-dimensional variable weight

2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS) ◽

10.1109/icsess.2016.7883093 ◽

2016 ◽

Author(s):

Bai Yaxin ◽

Zhang Hong ◽

Feng Chao ◽

Fu Yangzhen

Keyword(s):

Service Selection ◽

Selection Model ◽

Two Dimensional ◽

Composite Service ◽

Model Based ◽

Variable Weight ◽

Dimensional Variable

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CC-PSM: A Preference-Aware Selection Model for Cloud Service Based on Consumer Community

Mathematical Problems in Engineering ◽

10.1155/2015/170656 ◽

2015 ◽

Vol 2015 ◽

pp. 1-13 ◽

Cited By ~ 4

Author(s):

Yan Wang ◽

Jian-tao Zhou ◽

Hong-yan Tan

Keyword(s):

Clustering Algorithm ◽

Service Selection ◽

Cloud Service ◽

Selection Model ◽

Bipartite Network ◽

Model Based ◽

Selection Strategies ◽

Hierarchical Clustering Algorithm ◽

Full Consideration

In order to give full consideration to the consumer’s personal preference in cloud service selection strategies and improve the credibility of service prediction, a preference-aware cloud service selection model based on consumer community (CC-PSM) is presented in this work. The objective of CC-PSM is to select a service meeting a target consumer’s demands and preference. Firstly, the correlation between cloud consumers from a bipartite network for service selection is mined to compute the preference similarity between them. Secondly, an improved hierarchical clustering algorithm is designed to discover the consumer community with similar preferences so as to form the trusted groups for service recommendation. In the clustering process, a quantization function called community degree is given to evaluate the quality of community structure. Thirdly, a prediction model based on consumer community is built to predict a consumer’s evaluation on an unknown service. The experimental results show that CC-PSM can effectively partition the consumers based on their preferences and has good effectiveness in service selection applications.

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