Quality of Experience User’s Perception about Web Services - 2015 PROJECT TITLE: Quality of Experience User’s Perception about Web Services - 2015 ABSTRACT: Internet service composition allows seamless and dynamic integration of Web services. The behavior of participant Web services determines the performance of a composition. Therefore, it is necessary to settle on top quality services for service composition. Existing Web service choice and discovery approaches depend upon non-purposeful aspects (additionally called quality of service or QoS), e.g., response time and availability. Though these parameters are crucial for selecting.Net services, they will not reflect the user's perspective of quality. In this paper, we tend to explore the feasibility of incorporating perceived quality from user's perspective for service choice and composition. We tend to name such quality attributes as quality of experience (QoE). 1st, we tend to propose a resolution that automatically mines and identifies QoE attributes from the Web. Second, we study the application of such dynamically extracted QoE attributes for service selection. For the analysis purpose, we collected a lot of than thirty four,000 reviews from fifty eight completely different services in six domains. Our findings show that it is doable to automatically identify QoE attributes with an average precision and recall of 92 and 80 % respectively. Our study shows that there is a sturdy positive correlation between QoS and QoE. Hence QoE will be used throughout service selection especially when QoS knowledge aren't on the market. Furthermore, we found 70 p.c of service discovery queries indeed contain QoE attributes showing the importance of QoE attributes throughout the service discovery phase. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Web Mining Projects Real-Time Detection of Traffic From Twitter Stream Analysis - 2015 Network-Based Modeling and Intelligent Data Mining of Social Media for Improving Care - 2015