Classification of web services using data mining algorithms and improved learning model

As per the global digital report, 52.9% of the world population is using the internet, and 42% of the world population is actively using e-commerce, banking, and other online applications. Web servicesare software components accessed using networked communications and provide services to end users. Software developers provide a high quality of web service. To meet the demands of user requirements, it is necessary for a developer to ensure quality architecture and quality of services. To meet the demands of user measure service quality by the ranking of web services, in this paper, we analyzed QWS datasetand found important parameters are best practices, successability, availability, response time, reliability and throughput, and compliance. We have used various data mining techniques and conductedexperiments to classify QWS data set into four categorical values as class1, 2, 3, and 4. The results are compared with various techniques random forest, artificial neural network, J48 decision tree, extremegradient boosting, K-nearest neighbor, and support vector machine. Multiple classifiers analyzed, and it was observed that the classifier technique eXtreme gradient boosting got the maximum accuracy of98.44%, and random forest got the accuracy of 98.13%. In future, we can extend the quality of web service for mixed attributes

Multi-Objective Service Composition in Ubiquitous Environments with Service Dependencies

International audienceService composition is a widely used method in ubiquitous computing that enables accomplishing complex tasks required by users based on elementary (hardware and software) services available in ubiquitous environments. To ensure that users experience the best Quality of Service (QoS) with respect to their quality needs, service composition has to be QoS-aware. Establishing QoS-aware service compositions entails efficient service selection taking into account the QoS requirements of users. A challenging issue towards this purpose is to consider service selection under global QoS requirements (i.e., requirements imposed by the user on the whole task), which is of high computational cost. This challenge is even more relevant when we consider the dynamics, limited computational resources and timeliness constraints of ubiquitous environments. To cope with the above challenge, we present QASSA, an efficient service selection algorithm that provides the appropriate ground for QoS-aware service composition in ubiquitous environments. QASSA formulates service selection under global QoS requirements as a set-based optimisation problem, and solves this problem by combining local and global selection techniques. In particular, it introduces a novel way of using clustering techniques to enable fine-grained management of trade-offs between QoS objectives. QASSA further considers: (i) dependencies between services, (ii) adaptation at run-time, and (iii) both centralised and distributed design fashions. Results of experimental studies performed using real QoS data are presented to illustrate the timeliness and optimality of QASSA