The first issue was focused on a variety of topics in formulation and algorithmic approaches that are fundamental to data analysis, both in formulating and solving problems.
Piscataway, NJ (PRWEB) March 08, 2017
Proceedings of the IEEE, one of the most highly cited general-interest journals in electrical and computer engineering and computer science, has published the second of two special issues focusing on the practical applications of big data.
The second issue consists of seven papers covering a variety of topics around big data and the Internet of Things (IoT).
“The first issue was focused on a variety of topics in formulation and algorithmic approaches that are fundamental to data analysis, both in formulating and solving problems,” said Simon Haykin, IEEE Fellow and University Professor in the Faculty of Engineering at McMaster University in Canada. “This issue is devoted to practical applications of big data. The two complement each other and recognizes we’ve entered the fourth industrial revolution.”
Haykin and the co-guest editors Volker Tresp, Professor at Ludwig Maximilian University of Munich and Principal Research Scientist at Siemens, and Jon Atli Benediktsson, IEEE Fellow, President of Science and Academic Affairs, and Professor of Electrical and Computer Engineering at the University of Iceland, open the issue with a paper from Josep Blat, et al, presenting solutions to improve the integration of multiple data sources in media production, and understand their quality and content. This would be useful to support creative decisions on set and enhance the production process.
Other papers in the special issue cover contributions that address various studies, including:
- A survey that categorizes the existing parallel data processing systems for big data and introduces representative projects widely researched by academic and industry experts. Topics cover extensions and optimization for MapReduce platform, stream processing systems and graph-centric systems, among others.
- The classification of big data with a focus on classification of 3-D MRI images of the human brain in a genetic context. The proposed method in the paper estimates the covariance by using a sparse version of noisy principal component analysis (nPCA). The method compares well to other state-of-the-art big data methods.
- This paper looks at the activities and progress of the big data assimilation project for severe weather prediction and concludes with perspectives toward post-petascale supercomputing era. As sensor and computer technologies advance, orders of magnitude bigger data are produced by new sensors and high-precision computer simulation or “big simulation.” The current numerical weather prediction (NWP) systems are not designed to handle “big data.”
- The current healthcare system trends toward digitalization and large-scale data analytics in healthcare. While many challenges remain, this paper reviews different projects and business models for potential in large-scale data analytics for better and more effective patient care.
- Big data can bring challenges and opportunities in the context of remote sensing opportunities. One of the papers explores how big data is used to automatically detect marine-oil spills using a large archive of remote sensing data. It also focuses on a test case where content-based information retrieval is performed using high performance computing to extract information from a large database of remote sensing images.
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