Downstream Consent: A Better Legal Framework for Big Data

Better Data Beat Big Data

Generalizability of models of student learning is a highly desirable feature. As new students interact with educational systems, highly predictive models, tuned to increasing amounts of data from previous learners, presumably allow such systems to provide a more individualized, optimal learning path, give better feedback, and provide a more effective learning experience. However, any large stud...

A Fuzzy TOPSIS Approach for Big Data Analytics Platform Selection

Big data sizes are constantly increasing. Big data analytics is where advanced analytic techniques are applied on big data sets. Analytics based on large data samples reveals and leverages business change. The popularity of big data analytics platforms, which are often available as open-source, has not remained unnoticed by big companies. Google uses MapReduce for PageRank and inverted indexes....

A Privacy Engineering Framework for Big Data

In the Big Data era, the collection and mining of high-dimensional user data has become a fast growing and common practice by a large number of private and public institutions. In some cases, these institutions share data with third parties without an anonymity preferring user’s consent or awareness, thereby risking his privacy. In other cases, data is released voluntarily by a user not necessa...

A Novel Integrated Framework to Ensure Better Data Quality in Big Data Analytics over Cloud Environment

Received Mar 23, 2017 Revised Jun 18, 2017 Accepted Sept 11, 2017 With advent of Big Data Analytics, the healthcare system is increasingly adopting the analytical services that is ultimately found to generate massive load of highly unstructured data. We reviewed the existing system to find that there are lesser number of solutions towards addressing the problems of data variety, data uncertaint...

A Data-as-a-Service Framework for IoT Big Data