Web service selection with global constraints using modified gray wolf optimizer

2016 ◽  
Author(s):  
Manik Chandra ◽  
Ashutosh Agrawal ◽  
Avadh Kishor ◽  
Rajdeep Niyogi
Keyword(s):  
Web Service ◽  
Service Selection ◽  
Global Constraints ◽  
Gray Wolf ◽  
Web Service Selection ◽  
Gray Wolf Optimizer

Method of Top-k Web service selection based on QoS

2010 ◽  
Vol 30 (4) ◽  
pp. 872-875
Author(s):  
Hai WANG ◽  
Zheng-dong ZHU ◽  
Zeng-zhi LI
Keyword(s):  
Web Service ◽  
Service Selection ◽  
Web Service Selection

Multi-user web service selection based on multi-QoS prediction

2013 ◽  
Vol 16 (1) ◽  
pp. 143-152 ◽  
Author(s):  
Shangguang Wang ◽  
Ching-Hsien Hsu ◽  
Zhongjun Liang ◽  
Qibo Sun ◽  
Fangchun Yang
Keyword(s):  
Web Service ◽  
Service Selection ◽  
Qos Prediction ◽  
Web Service Selection

scholarly journals A Cluster-Based Browsing Model For QoS-Aware Web Service Selection

2021 ◽  
Author(s):  
Kian Farsandaj
Keyword(s):  
Web Services ◽  
Web Service ◽  
Clustering Algorithm ◽  
Service Selection ◽  
Functional Requirements ◽  
Analysis Techniques ◽  
Symbolic Data ◽  
Web Service Selection ◽  
The Web

In the last decade, selecting suitable web services based on users’ requirements has become one of the major subjects in the web service domain. Any research works have been done - either based on functional requirements, or focusing more on Quality of Service (QoS) - based selection. We believe that searching is not the only way to implement the selection. Selection could also be done by browsing, or by a combination of searching and browsing. In this thesis, we propose a browsing method based on the Scatter/Gather model, which helps users gain a better understanding of the QoS value distribution of the web services and locate their desired services. Because the Scatter/Gather model uses cluster analysis techniques and web service QoS data is best represented as a vector of intervals, or more generically a vector of symbolic data, we apply for symbolic clustering algorithm and implement different variations of the Scatter/Gather model. Through our experiments on both synthetic and real datasets, we identify the most efficient ( based on the processing time) and effective implementations.

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