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