Frequent Pattern Mining Using Semantic FP-Growth for Effective Web Service Ranking

Automated Ranking is crucial in the process of automated Web Services execution. Often adaptation and ranking (used interchangeably) of discovered Web services is carried out using functional and non-functional information of Web Services. Existing approaches are either found to be only focusing on semantic modeling and representation only, or using data mining and machine learning based approaches on unstructured and raw data to perform discovery and ranking. We propose an approach to allow semantically formalized representation of logs during Web Service execution and then use such logs to perform ranking and adaptation of discovered Web Services. We have built Semantic FP-Tree based technique to perform association rule learning on functional and non-functional characteristics of Web Services. The process of automated execution of Web Services is improved in two steps, i.e., (1) we provide semantically formalized logs that maintain well-structured and formalized information about past interactions of Services Consumers and Web Services, (2) we perform an extended association rule mining on semantically formalized logs to find out any possible correlation in functional and non-functional characteristics of Web Services during past execution which is then used in automated ranking and adaptation of Web Services.