Big Data Analytics for Sensor-Network Collected Intelligence
- 1st Edition - February 2, 2017
- Imprint: Academic Press
- Editors: Hui-Huang Hsu, Chuan-Yu Chang, Ching-Hsien Hsu
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 0 9 3 9 3 - 1
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 0 9 6 2 5 - 3
Big Data Analytics for Sensor-Network Collected Intelligence explores state-of-the-art methods for using advanced ICT technologies to perform intelligent analysis on sensor co… Read more
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Request a sales quoteBig Data Analytics for Sensor-Network Collected Intelligence explores state-of-the-art methods for using advanced ICT technologies to perform intelligent analysis on sensor collected data. The book shows how to develop systems that automatically detect natural and human-made events, how to examine people’s behaviors, and how to unobtrusively provide better services.
It begins by exploring big data architecture and platforms, covering the cloud computing infrastructure and how data is stored and visualized. The book then explores how big data is processed and managed, the key security and privacy issues involved, and the approaches used to ensure data quality.
In addition, readers will find a thorough examination of big data analytics, analyzing statistical methods for data analytics and data mining, along with a detailed look at big data intelligence, ubiquitous and mobile computing, and designing intelligence system based on context and situation.
Indexing: The books of this series are submitted to EI-Compendex and SCOPUS
- Contains contributions from noted scholars in computer science and electrical engineering from around the globe
- Provides a broad overview of recent developments in sensor collected intelligence
- Edited by a team comprised of leading thinkers in big data analytics
Big Data and Networking researchers, practitioners, and upper level and graduate students
Part I: Big Data Architecture and Platforms
Chapter 1: Big Data: A Classification of Acquisition and Generation Methods
- Abstract
- 1 Big Data: A Classification
- 2 Big Data Generation Methods
- 3 Big Data: Data Acquisition Methods
- 4 Big Data: Data Management
- 5 Summary
- Glossary
Chapter 2: Cloud Computing Infrastructure for Data Intensive Applications
- Abstract
- Acknowledgments
- 1 Introduction
- 2 Big Data Nature and Definition
- 3 Big Data and Paradigm Change
- 4 Big Data Architecture Framework and Components
- 5 Big Data Infrastructure
- 6 Case Study: Bioinformatics Applications Deployment on Cloud
- 7 CYCLONE Platform for Cloud Applications Deployment and Management
- 8 Cloud Powered Big Data Applications Development and Deployment Automation
- 9 Big Data Service and Platform Providers
- 10 Conclusion
- Glossary
Chapter 3: Open Source Private Cloud Platforms for Big Data
- Abstract
- 1 Cloud Computing and Big Data as a Service
- 2 On-Premise Private Clouds for Big Data
- 3 Introduction to Selected Open Source Cloud Environments
- 4 Heterogeneous Computing in the Cloud
- 5 Case Study: The EMS, an On-Premise Private Cloud
- 6 Conclusion
- Disclaimer
Part II: Big Data Processing and Management
Chapter 4: Efficient Nonlinear Regression-Based Compression of Big Sensing Data on Cloud
- Abstract
- 1 Introduction
- 2 Related Work and Problem Analysis
- 3 Temporal Compression Model Based on Nonlinear Regression
- 4 Algorithms
- 5 Experiments
- 6 Conclusions and Future Work
Chapter 5: Big Data Management on Wireless Sensor Networks
- Abstract
- 1 Introduction
- 2 Data Management on WSNs
- 3 Big Data Tools
- 4 Put It Together: Big Data Management Architecture
- 5 Big Data Management on WSNs
- 6 Conclusion
- Glossary
Chapter 6: Extreme Learning Machine and Its Applications in Big Data Processing
- Abstract
- 1 Introduction
- 2 Extreme Learning Machine
- 3 Improved Extreme Learning Machine With Big Data
- 4 Applications
- 5 Conclusion
- Glossary
Part III: Big Data Analytics and Services
Chapter 7: Spatial Big Data Analytics for Cellular Communication Systems
- Abstract
- Acknowledgments
- 1 Introduction
- 2 Cellular Communications and Generated Data
- 3 Spatial Big Data Analytics
- 4 Typical Applications
- 5 Conclusion and Future Challenging Issues
- Glossary
Chapter 8: Cognitive Applications and Their Supporting Architecture for Smart Cities
- Abstract
- 1 Introduction
- 2 CSE for Smart City Applications
- 3 Anomaly Detection in Smart City Management
- 4 Functional Region and Socio-Demographic Regional Patterns Detection in Cities
- 5 Summary
- Glossary
Chapter 9: Deep Learning for Human Activity Recognition
- Abstract
- 1 Introduction
- 2 Motivations and Related Work
- 3 Convolutional Neural Networks in HAR
- 4 Experiments, Results, and Discussion
- 5 Conclusion
- Glossary
Chapter 10: Neonatal Cry Analysis and Categorization System Via Directed Acyclic Graph Support Vector Machine
- Abstract
- Acknowledgment
- 1 Introduction
- 2 Neonatal Cry Analysis and Categorization System
- 3 Experimental Results and Discussion
- 4 Conclusion
Part IV: Big Data Intelligence and IoT Systems
Chapter 11: Smart Building Applications and Information System Hardware Co-Design
- Abstract
- 1 Smart Building Applications
- 2 Emerging Information System Hardware
- 3 Big Data Application and Information Hardware Co-design
- 4 Conclusions
- Glossary
Chapter 12: Smart Sensor Networks for Building Safety
- Abstract
- Acknowledgments
- 1 Introduction
- 2 Related Works
- 3 Background: Modal Analysis
- 4 Distributed Modal Analysis
- 5 A Multiscale SHM Using Cloud
- 6 Conclusion
- Glossary
Chapter 13: The Internet of Things and Its Applications
- Abstract
- 1 Introduction
- 2 Collection of Big Data From IoT
- 3 IoT Analytics
- 4 Examples of IoT Applications
- 5 Conclusions
- Glossary
Chapter 14: Smart Railway Based on the Internet of Things
- Abstract
- Acknowledgment
- 1 Introduction
- 2 Architecture of the Smart Railway
- 3 IRIS for Smart Railways
- 4 Conclusion
- Glossary
- Edition: 1
- Published: February 2, 2017
- No. of pages (Paperback): 326
- No. of pages (eBook): 326
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780128093931
- eBook ISBN: 9780128096253
HH
Hui-Huang Hsu
Hui-Huang Hsu is a Professor in the Department of Computer Science and Information Engineering at Tamkang University in Taiwan. He also serves as the Dean of College of Engineering since August 2016. Previously, he was the Chairman of the Department. Prof. Hsu received both his Ph.D. and M.S. Degrees from the Department of Electrical and Computer Engineering at the University of Florida, USA. He got his B.E. degree in Electrical Engineering from Tamkang University. He has worked in the areas of machine learning, data mining, ambient intelligence, bio-medical informatics, and multimedia processing. Prof. Hsu is a senior member of the IEEE. He is also an Executive Board Member of Taiwanese Association for Artificial Intelligence (TAAI).
Affiliations and expertise
Tamkang University, TaiwanCC
Chuan-Yu Chang
Chuan-Yu Chang is Distinguished Professor and Dean of Research and Development at National Yunlin University of Science and Technology, Taiwan. He has more than 150 publications in journals and conference proceedings, and his research interests include machine learning, medical image processing, wafer defect inspection, digital watermarking, and pattern recognition.
Affiliations and expertise
National Yunlin University of Science and Technology, TaiwanCH
Ching-Hsien Hsu
Ching-Hsien Hsu is a Professor in Department of Computer Science and Information Engineering at Chung Hua University, Taiwan. His research includes cloud computing, big data analytics, parallel and distributed systems, high performance computing, ubiquitous/pervasive computing and intelligence. Dr. Hsu is the Editor-in-Chief of International Journal of Grid and High Performance Computing and International Journal of Big Data Intelligence and serves as on the editorial board of a number of other journals. He has published 250 papers in refereed journals and conference proceedings and served as an author or editor of 10 books.
Affiliations and expertise
Chung Hua University, TaiwanRead Big Data Analytics for Sensor-Network Collected Intelligence on ScienceDirect