Overview
- Presents the latest developments in data science algorithms, the output of which is highlighted in terms of the approaches to mining Big Data pursued by programmers, scientists, and managers
- Documents the machine learning hypothesis and data mining tasks and covers computational benchmarks and parametric models produced by both academia and industry
- Discusses the interface of Big Data Analytics and Data-Driven Computing with reference to Large-Scale Pattern Recognition
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Table of contents (17 chapters)
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Front Matter
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Back Matter
Keywords
About this book
This book provides a comprehensive survey of techniques, technologies and applications of Big Data and its analysis. The Big Data phenomenon is increasingly impacting all sectors of business and industry, producing an emerging new information ecosystem. On the applications front, the book offers detailed descriptions of various application areas for Big Data Analytics in the important domains of Social Semantic Web Mining, Banking and Financial Services, Capital Markets, Insurance, Advertisement, Recommendation Systems, Bio-Informatics, the IoT and Fog Computing, before delving into issues of security and privacy.
With regard to machine learning techniques, the book presents all the standard algorithms for learning – including supervised, semi-supervised and unsupervised techniques such as clustering and reinforcement learning techniques to perform collective Deep Learning. Multi-layered and nonlinear learning for Big Data are also covered.
In turn,the book highlights real-life case studies on successful implementations of Big Data Analytics at large IT companies such as Google, Facebook, LinkedIn and Microsoft. Multi-sectorial case studies on domain-based companies such as Deutsche Bank, the power provider Opower, Delta Airlines and a Chinese City Transportation application represent a valuable addition.
Given its comprehensive coverage of Big Data Analytics, the book offers a unique resource for undergraduate and graduate students, researchers, educators and IT professionals alike.
With regard to machine learning techniques, the book presents all the standard algorithms for learning – including supervised, semi-supervised and unsupervised techniques such as clustering and reinforcement learning techniques to perform collective Deep Learning. Multi-layered and nonlinear learning for Big Data are also covered.
In turn,the book highlights real-life case studies on successful implementations of Big Data Analytics at large IT companies such as Google, Facebook, LinkedIn and Microsoft. Multi-sectorial case studies on domain-based companies such as Deutsche Bank, the power provider Opower, Delta Airlines and a Chinese City Transportation application represent a valuable addition.
Given its comprehensive coverage of Big Data Analytics, the book offers a unique resource for undergraduate and graduate students, researchers, educators and IT professionals alike.
Authors and Affiliations
About the authors
Dr. Chivukula Sree Rama Prabhu has held prestigious positions with Government of India and various institutions. He retired as Director General of the National Informatics Centre (NIC), Ministry of Electronics and Information Technology, Government of India, New Delhi, and has worked with Tata Consultancy Services (TCS), CMC, TES and TELCO (now Tata Motors). He was also faculty for the Programs of the APO (Asian Productivity Organization). He has taught and researched at the University of Central Florida, Orlando, USA, and also had a brief stint as a Consultant to NASA. He was Chairman of the Computer Society of India (CSI), Hyderabad Chapter. He is presently working as an Advisor (Honorary) at KL University, Vijayawada, Andhra Pradesh, and as a Director of Research and Innovation at Keshav Memorial Institute of Technology (KMIT), Hyderabad.
He received his Master’s degree in Electrical Engineering with specialization in Computer Science from the Indian Institute of Technology, Bombay. He has guided many Master’s and doctoral students in research areas such as Big Data.Dr. Aneesh Sreevallabh Chivukula is currently a Research Scholar at the Advanced Analytics Institute, University of Technology Sydney (UTS), Australia. Previously, he chiefly worked in computational data science-driven product development at Indian startup companies and research labs. He received his M.S. degree from the International Institute of Information Technology (IIIT), Hyderabad. His research interests include machine learning, data mining, pattern recognition, big data analytics and cloud computing.
Dr. Aditya Mogadala is a postdoc in the Language Science and Technology at Saarland University. His research concentrates on the general area of Deep/Representation learning applied for integration of external real-world/common-sense knowledge (e.g., vision and knowledge graphs) into natural language sequence generation models. Before Postdoc, he was a PhD student and Research Associate at the Karlsruhe Institute of Technology, Germany. He holds B.Tech and M.S. degree from the IIIT, Hyderabad, and has worked as a Software Engineer at IBM India Software Labs.
Mr. Rohit Ghosh currently works at Qure, Mumbai. He previously served as a Data Scientist for ListUp, and for Data Science Labs. Holding a B.Tech. from the IIT Mumbai, his work involves R&D areas in computer vision, deep learning, reinforcement learning (mostly related to trading strategies) and cryptocurrencies.
Dr. Jenila Livingston is an Associate Professor with the CSE Dept at VIT, Chennai. Her teaching foci and research interests include artificial intelligence, soft computing, and analytics.
He received his Master’s degree in Electrical Engineering with specialization in Computer Science from the Indian Institute of Technology, Bombay. He has guided many Master’s and doctoral students in research areas such as Big Data.Dr. Aneesh Sreevallabh Chivukula is currently a Research Scholar at the Advanced Analytics Institute, University of Technology Sydney (UTS), Australia. Previously, he chiefly worked in computational data science-driven product development at Indian startup companies and research labs. He received his M.S. degree from the International Institute of Information Technology (IIIT), Hyderabad. His research interests include machine learning, data mining, pattern recognition, big data analytics and cloud computing.
Dr. Aditya Mogadala is a postdoc in the Language Science and Technology at Saarland University. His research concentrates on the general area of Deep/Representation learning applied for integration of external real-world/common-sense knowledge (e.g., vision and knowledge graphs) into natural language sequence generation models. Before Postdoc, he was a PhD student and Research Associate at the Karlsruhe Institute of Technology, Germany. He holds B.Tech and M.S. degree from the IIIT, Hyderabad, and has worked as a Software Engineer at IBM India Software Labs.
Mr. Rohit Ghosh currently works at Qure, Mumbai. He previously served as a Data Scientist for ListUp, and for Data Science Labs. Holding a B.Tech. from the IIT Mumbai, his work involves R&D areas in computer vision, deep learning, reinforcement learning (mostly related to trading strategies) and cryptocurrencies.
Dr. Jenila Livingston is an Associate Professor with the CSE Dept at VIT, Chennai. Her teaching foci and research interests include artificial intelligence, soft computing, and analytics.
Bibliographic Information
Book Title: Big Data Analytics: Systems, Algorithms, Applications
Authors: C.S.R. Prabhu, Aneesh Sreevallabh Chivukula, Aditya Mogadala, Rohit Ghosh, L.M. Jenila Livingston
DOI: https://doi.org/10.1007/978-981-15-0094-7
Publisher: Springer Singapore
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Singapore Pte Ltd. 2019
Hardcover ISBN: 978-981-15-0093-0Published: 24 October 2019
Softcover ISBN: 978-981-15-0096-1Published: 24 October 2020
eBook ISBN: 978-981-15-0094-7Published: 14 October 2019
Edition Number: 1
Number of Pages: XXVI, 412
Number of Illustrations: 66 b/w illustrations, 108 illustrations in colour