Adoption of big data concept towards managing increasing data from the educational sector will have various benefits in terms of knowledge sharing process. The existing research approaches towards managing educational data in terms of high end analytics is either missing or is quite narrowed that doesn’t encapsulate the concept of big data implementation with practical problem. Therefore, this paper presents a discussion of a distributed framework that is capable of performing transformation of raw educational data arriving from distributed sources followed by applying a novel mining approach in order to ensure the final data with highest quality. The term quality interprets as a dynamic educational big data to be highly structured and free from any form of artifacts. Result analysis shows that proposed system offer faster transformation time and better data purity over synthetic educational big data.
<p>Content placement algorithm is an integral part of the cloud-based content de-livery network. They are responsible for selecting a precise content to be re-posited over the surrogate servers distributed over a geographical region. Although various works are being already carried out in this sector, there are loopholes connected to most of the work, which doesn't have much disclosure. It is already known that quality of service, quality of experience, and the cost is always an essential objective targeting to be improved in existing work. Still, there are various other aspects and underlying reasons which are equally important from the design aspect. Therefore, this paper contributes towards reviewing the existing approaches of content placement algorithm over cloud-based content delivery network targeting to explore open-end re-search issues.</p>
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