Guide to Big Data Applications (Studies in Big Data, 26) 1st Edition by S. Srinivasan 9783319538167

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Product details: 

• ISBN-13:  9783319538167
• Author: S. Srinivasan

This handbook brings together a variety of approaches to the uses of big data in multiple fields, primarily science, medicine, and business. This single resource features contributions from researchers around the world from a variety of fields, where they share their findings and experience. This book is intended to help spur further innovation in big data. The research is presented in a way that allows readers, regardless of their field of study, to learn from how applications have proven successful and how similar applications could be used in their own field. Contributions stem from researchers in fields such as physics,   biology, energy, healthcare, and   business. The contributors also discuss important topics such as fraud detection, privacy implications, legal perspectives, and ethical handling of big data.

Table of contents:

Introduction.

– Big Data Analytics.

– Big Data and Social Media.

– Use of Cloud Computing for Big Data in Business.

– Economic Data Analysis Related to Developing Countries.

– High Performance Computing and Big Data.

– Big Data Applications in Physics.

– Big Data Applications in Chemistry.

– Big Data Applications in Mathematics.

– Big Data Applications in Biology.

– Big Data Applications in Engineering.

– Big Data Applications in Meteorology.

– Big Data Applications in Environmental Science.

– Big Data Applications in Energy.

– Security Applications for Big Data.

– Big Data Applications in Network Traffic Analysis.

– Big Data Applications in Supply Chain Logistics.

– Big Data Applications in Healthcare.

– Big Data Applications in Cancer Research.

– Impact of Big Data in Marketing.

– Use of Big Data in Banking.

– Using Big Data for Fraud Detection in Accounting.

– Using Big Data for Supply Chain Management.

– Privacy Implications of Big Data.

– Legal Perspectives of Big Data.

– Ethical Handling of Big Data in Practical Uses.

– Conclusio

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