QoS prediction algorithm used in location-aware hybrid Web service

doi:10.1016/S1005-8885(15)60623-9

Acta Metallurgica Sinica(English letters) ›› 2015, Vol. 22 ›› Issue (1): 42-49.doi: 10.1016/S1005-8885(15)60623-9

QoS prediction algorithm used in location-aware hybrid Web service

鄂海红¹,童俊杰¹,宋美娜²,宋俊德¹

1. 北京邮电大学
2.

收稿日期:2014-06-12 修回日期:2014-09-25 出版日期:2015-02-28 发布日期:2015-02-28
通讯作者: 鄂海红 E-mail:ehaihong@bupt.edu.cn
基金资助:
国家科技支撑计划;教育部博士点研究基金;北京高等学院青年英才计划项目

QoS prediction algorithm used in location-aware hybrid Web service

Received:2014-06-12 Revised:2014-09-25 Online:2015-02-28 Published:2015-02-28
Supported by:
National Key project of Scientific and Technical Supporting Programs of China;the Research Fund for the Doctoral Program of Higher Education;Beijing Higher Education Young Elite Teacher Project

摘要/Abstract

摘要： Quality-of-Service (QoS) describes the non-functional characteristics of Web services. As such, the QoS is a critical parameter in service selection, composition and fault tolerance, and must be accurately determined by some type of QoS prediction method. However, with the dramatic increase in the number of Web services, the prediction failure caused by data sparseness has become a critical challenge. A new ‘hybrid user-location-aware prediction based on weighted Adamic-Adar (WAA)’ (HUWAA) was proposed. The implicit neighbor search was optimized by incorporating location factors. Meanwhile, the ability of the improved algorithms to solve the data sparsity problem was validated in experiments on public real world datasets. The new algorithm outperforms the existing of item-based pearson correlation coefficient (IPCC), user-based pearson correlation coefficient (UPCC) and Web service recommender system (WSRec) algorithms.

关键词: service QoS prediction, data sparsity, link prediction, location-aware

Abstract: Quality-of-Service (QoS) describes the non-functional characteristics of Web services. As such, the QoS is a critical parameter in service selection, composition and fault tolerance, and must be accurately determined by some type of QoS prediction method. However, with the dramatic increase in the number of Web services, the prediction failure caused by data sparseness has become a critical challenge. A new ‘hybrid user-location-aware prediction based on weighted Adamic-Adar (WAA)’ (HUWAA) was proposed. The implicit neighbor search was optimized by incorporating location factors. Meanwhile, the ability of the improved algorithms to solve the data sparsity problem was validated in experiments on public real world datasets. The new algorithm outperforms the existing of item-based pearson correlation coefficient (IPCC), user-based pearson correlation coefficient (UPCC) and Web service recommender system (WSRec) algorithms.

Key words: service QoS prediction, data sparsity, link prediction, location-aware

中图分类号:

TP391

参考文献

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QoS prediction algorithm used in location-aware hybrid Web service

QoS prediction algorithm used in location-aware hybrid Web service

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