Sugata Sanyal
Big Data Analytics has become an often repeated name. I was curious as always about any new publications on the subject. When I got an invitation from Elsevier to review a now famous book,
Editor(s): Govindaraju & Raghavan & Rao
Release Date: 07 Jul 2015
Imprint: Elsevier
Print Book ISBN: 9780444634924
Pages: 390
tracking the evolution of Big Data, focusing on timely topics such as data mining and analytics
I was thrilled due to a few techno-academic reasons:
- This book is a compendium of various chapters on the theory of Big Data and its applications in real life issues.
- Editors and authors are all famous academicians, and the standard of the book is very high.
- Big Data Analytics is changing the way we handle various issues and it is yielding results, which were not possible a few years back.
- This subject is very dynamic; it is a science of science. And, it is ever expanding and some new applications are becoming tractable due to Big Data Analytics.
Some details:
The book was edited, by Profs. Venu Givindaraju, Vijay Raghavan and C. R. Rao, on Big Data Analytics and is now available with Elsevier.
Details:
http://store.elsevier.com/product.jsp?isbn=9780444634924&pagename=search
A brief description below:
While the term Big Data is open to varying interpretations, it is quite clear that the Volume, Velocity, and Variety (3Vs) of data have impacted every aspect of computational science and its applications. The volume of data is increasing at a phenomenal rate and a majority of it is unstructured. With big data, the volume is so large that processing it using traditional database and software techniques is difficult, if not impossible. The drivers are the ubiquitous sensors, devices, social networks and the all-pervasive web. Scientists are increasingly looking to derive insights from the massive quantity of data to create new knowledge. In common usage, Big Data has come to refer simply to the use of predictive analytics or other certain advanced methods to extract value from data, without any required magnitude thereon. Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy. While there are challenges, there are also huge opportunities emerging in the fields of Machine Learning, Data Mining, Statistics, Human-Computer Interfaces, and Distributed Systems to address ways to analyze and reason with this data. The edited volume focuses on the challenges and opportunities posed by “Big Data” in a variety of domains and how statistical techniques and innovative algorithms can help glean insights and accelerate discovery. Big data has the potential to help companies improve operations and make faster, intelligent decisions.
I wish and hope that you enjoy Big Data Analytics and its applications as much as I did.