Abstract Web services composition is a process that involves building new applications or services called composite services by assembling existing services. With the technological evolution and the mobility of the devices and thus the varieties of the environments, the development of the approaches sensitive to the context is paramount. This paper presents a comparative analysis of existing approaches of context-aware semantic Web orchestration in... Web services composition is a process that involves building new applications or services called composite services by assembling existing services. With the technological evolution and the mobility of the devices and thus the varieties of the environments, the development of the approaches sensitive to the context is paramount. This paper presents a comparative analysis of existing approaches of context-aware semantic Web orchestration in mobile environments, and provides conclusions. Read More Read Less

Abstract A smart city requires the intelligent management of infrastructure like the Internet of Things (IoT) devices in order to provide smart services that improve the quality of human life. To obtain the information needed to implement smart city services, stream reasoning is used to intelligently process the big data stream constantly generated from IoT devices. However, there are constraints associated with the real-time... A smart city requires the intelligent management of infrastructure like the Internet of Things (IoT) devices in order to provide smart services that improve the quality of human life. To obtain the information needed to implement smart city services, stream reasoning is used to intelligently process the big data stream constantly generated from IoT devices. However, there are constraints associated with the real-time processing of large streams of big data from the smart city infrastructure. In this paper, we propose a stream reasoning system model for the smart city application, which was implemented using real-time big data processing technology in the smart city middleware. We use Apache Kafka, a message processing system, and Apache Storm, a real-time distributed processing system, to overcome the constraints associated with real-time processing. We evaluate the performance of our system implementation by measuring the throughput per second and the maximum capacity of the experimental system. Read More Read Less

Abstract Today, data is evolving at a massive rate and social media contributes to it by adding, people’s views, ideas, interests, day today happenings etc. Hence, analyzing social media contents will excavate knowledge treasures in the form of customer behavior, feedback, suggestions, opinions etc which could be utilized for business intelligence. Even though, sentiment analysis from social media is a severely explored domain, this... Today, data is evolving at a massive rate and social media contributes to it by adding, people’s views, ideas, interests, day today happenings etc. Hence, analyzing social media contents will excavate knowledge treasures in the form of customer behavior, feedback, suggestions, opinions etc which could be utilized for business intelligence. Even though, sentiment analysis from social media is a severely explored domain, this article studies the importance of “Time of posting the contents”. There are several real world applications where mere sentiment analysis is not sufficient and ‘time’ at which that content is posted should be given a weightage. For eg., to compare two products or to do trend analysis, recent posts should be given more weightage. The authors introduce a novel and scalable algorithm to introduce the time factor to improve the accuracy of sentiment analysis. To support Big Data, Hadoop Map Reduce based implementation is provided. To prove the efficiency of the method, Delhi Assembly Election Winner Prediction by Twitter Analysis is taken as a case study. The results prove that, our algorithm is accurate, scalable and time efficient as compared to the existing ones. Read More Read Less