Paper Info
Title
Geographic Location-Based Service Reliability Prediction
Abstract
How to design an effective and efficient reliability prediction method for services is one of the important topics in the research field of Services Computing. With the increasing complexity of network environments, the effect of network environments and other environments related properties on service reliability cannot be neglected any more. However, most of the existing reliability prediction methods focus on the service itself, and have not paid enough attention to the potential impact of external factors on service reliability, which leads to the result that accuracy of predicted reliability of services cannot be guaranteed. To address the problems above, a geographic location-based reliability prediction method (GLBRP) for services is proposed in this paper. Mapping techniques and Pearson Correlation Coefficient are used to classify users based on users' geographic location information. Deductive reasoning and ontology as well as the calculation of similarity are also employed to clarify services based on services' location information. Effective feedback can be extracted based on the grouping of users and services. Service reliability is predicted through the smoothing forecasting method of weighted moving series on effective feedback. Simulation results show that the proposed GLBRP method can significantly improve accuracy and efficiency of prediction results compared with other methods.
Year
DOI
Venue
2014
10.1109/.42
CBD
Keywords
Field
DocType
service,reliability,prediction,feedback
Data mining,Ontology,Services computing,Pearson product-moment correlation coefficient,Location,Computer science,Smoothing,Deductive reasoning,Web service,Software quality
Conference
ISSN
Citations 
PageRank 
1066-6192
0
0.34
References 
Authors
12
2
Name
Order
Citations
PageRank
Haiyan Wang101.35
jun qian201.01