Reputation in communities of agent-based web services through data mining

Abstract

We present in this paper a reputation model for agent-based web services grouped into communities by their equivalent functionalities. The reputation of each web service is based on the non-functional properties of its interactions with other web services from the same community. We exploit various clustering and anomaly detection techniques to analyze and identify the quality patterns provided by each service. This enables the master of each community to allocate the requests it receives to the web service that best fulfill the quality requirements of the service consumers. Our experiments present realistic scenarios based on synthetic data that characterizes the reputation feedback of the quality provided by a web service at different times. The results showcase the capability of our reputation model in portraying the quality of web services that reside in a community and characterizing their fair and unfair feedback reports.