The work of A. Dridi was supported by the Faculty of Computing, Engineering and Built Environment, Birmingham City University, through a Full Bursary Ph.D. Scholarship.
Cloud computing is gathering significant momentum in business and academia through the rich benefits it offers. It is apparent from the literature that both businesses and academic institutions would benefit greatly from the adoption of cloud technology, providing the challenges presented are overcome. This paper aims to review prevalent literature on cloud computing; presenting an initial comprehensive insight into how cloud technology is transforming businesses and the wider Information Technology (IT) industry in general; the service deployment models of Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS); and a discussion of perceived benefits and challenges of cloud adoption. The paper's core focus addresses the domain of Education as an area of cloud application with a cloud based e-learning system developed to demonstrate the capabilities and effectiveness of cloud technology. The last section of the paper offers a conclusion, discussing how cloud computing will evolve hereafter along with recommendations for furthering our research work.
Social media is becoming a primary medium to discuss what is happening around the world. Therefore, the data generated by social media platforms contain rich information which describes the ongoing events. Further, the timeliness associated with these data is capable of facilitating immediate insights. However, considering the dynamic nature and high volume of data production in social media data streams, it is impractical to filter the events manually and therefore, automated event detection mechanisms are invaluable to the community. Apart from a few notable exceptions, most previous research on automated event detection have focused only on statistical and syntactical features in data and lacked the involvement of underlying semantics which are important for effective information retrieval from text since they represent the connections between words and their meanings. In this paper, we propose a novel method termed Embed2Detect for event detection in social media by combining the characteristics in word embeddings and hierarchical agglomerative clustering. The adoption of word embeddings gives Embed2Detect the capability to incorporate powerful semantical features into event detection and overcome a major limitation inherent in previous approaches. We experimented our method on two recent real social media data sets which represent the sports and political domain and also compared the results to several state-of-the-art methods. The obtained results show that Embed2Detect is capable of effective and efficient event detection and it outperforms the recent event detection methods. For the sports data set, Embed2Detect achieved 27% higher F-measure than the best-performed baseline and for the political data set, it was an increase of 29%.
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