Computing for Data Analysis: Theory and Practices

✍ Scribed by Sanjay Chakraborty, Lopamudra Dey

• Publisher: Springer
• Year: 2023
• Tongue: English
• Leaves: 230
• Series: Data-Intensive Research
• Category: Library

✦ Synopsis

This book covers various cutting-edge computing technologies and their applications over data. It discusses in-depth knowledge on big data and cloud computing, quantum computing, cognitive computing, and computational biology with respect to different kinds of data analysis and applications. In this book, authors describe some interesting models in the cloud, quantum, cognitive, and computational biology domains that provide some useful impact on intelligent data (emotional, image, etc.) analysis. They also explain how these computing technologies based data analysis approaches used for various real-life applications. The book will be beneficial for readers working in this area.

✦ Table of Contents

Preface
Acknowledgements
Contents
About the Authors
1 Introduction
1.1 Data and Analysis
1.1.1 Types of Data
1.1.2 Analysis of Data
1.2 Big Data and Data Analytics
1.2.1 Big Data Architecture and Data Analysis
1.3 Cloud Computing and Data Analysis
1.4 Internet of Things (IoT) and Data Analysis
1.5 AR/VR and Data Analysis
1.6 Biological Computing and Data Analysis
1.6.1 Steps in Data Analysis
1.7 Cognitive Computing and Data Analysis
1.8 Quantum Computing and Data Analysis
1.8.1 Quantum-Inspired Data Analytics
1.9 Conclusion
References
Part I Integration of Cloud, Internet of Things, Virtual Reality and Big Data Analytics
2 Impact of Big Data and Cloud Computing on Data Analysis
2.1 Big Data Architecture with Hadoop and MapReduce
2.1.1 Hadoop Architecture
2.2 Big Data Analytics: Emerging Applications in Industry
2.3 Cloud Computing: Definition, Models, and Architectures
2.4 Comparison of Cloud with Other Computing
2.4.1 Cloud Versus Grid Versus Utility Computing
2.4.2 Cloud Models
2.4.3 Cloud Architecture
2.5 Load Balancing and Virtualization in Cloud Computing
2.5.1 Virtualization
2.5.2 Load Balancing
2.6 Cloud Computing Systems for Data-Intensive Applications
2.7 Analytical and Perspective Approach of Big Data in Cloud Computing
2.8 Conclusion
References
3 Edge Computing with Internet of Things (IoT) and Data Analysis
3.1 Introduction
3.2 Related Technologies, Architectures, and Protocols of IoT
3.2.1 IoT Architectures
3.3 Industry Applications of IoT
3.4 Big Data Analytics via IoT with Cloud Service
3.4.1 Data Acquisition, Preprocessing, and Storage
3.4.2 Computing in Cloud Framework for IoT
3.5 Conclusion
References
4 Virtual and Augmented Reality with Embedded Systems
4.1 Introduction
4.2 Types of Augmented Reality Systems
4.3 Overview of Augmented Reality System Organization
4.3.1 History of Augmented Reality
4.3.2 Embedded Systems Design Approaches
4.3.3 Custom AR
4.4 Augmented Reality Components
4.4.1 Hardware Components
4.4.2 Required Software
4.4.3 Remote Servers
4.5 Relation of 5G/6G with AR/VR Systems
4.6 Applications and Future Research Directions
4.6.1 Applications in AR
4.6.2 Future Research Directions
4.7 Conclusion
References
Part II Biological Applications of Data Analytics
5 Computational Biology Toward Data Analysis
5.1 Introduction
5.2 History of Computational Biology
5.3 Biological Data Types
5.4 Biological Databases
5.5 Data Analysis
5.5.1 DNA/RNA Sequence Data Analysis
5.5.2 Microarray Data Analysis and Preprocessing
5.5.3 Protein Sequences Data Analysis
5.6 Conclusion
References
6 Data Classification Through Cognitive Computing
6.1 Introduction
6.2 Background
6.2.1 Basic Components of BCI
6.2.2 Feature Extraction
6.2.3 Feature Classification
6.3 Result Analysis
6.4 Open Research Problems
6.5 EEG Signal-Based Emotional Data Classification
6.5.1 Introduction
6.5.2 Methodology
6.5.3 Result Analysis
6.6 Conclusion
References
Part III Quantum Computing for Data Analysis
7 Quantum Computing in Machine Learning
7.1 Introduction
7.2 Quantum Hybrid Data Clustering
7.2.1 Methodology: Pseudo-steps of Proposed Quantum Clustering Process
7.2.2 Analysis of Results
7.2.3 Computational Complexity Analysis
7.2.4 Pros and Cons
7.3 Quantum Hybrid Feature Subset Selection
7.3.1 Methodology (HQFSA)
7.3.2 Result Analysis
7.3.3 Complexity Analysis
7.4 Conclusion
References
8 Quantum Computing in Image Processing
8.1 Introduction
8.2 Quantum Image Denoising
8.2.1 Background
8.2.2 Methodology
8.2.3 Analysis of Results
8.3 Quantum Image Edge Detection
8.3.1 Background
8.3.2 Analysis of Results
8.4 Conclusion
References
Part IV Computations for Various Data Applications and Future Work
9 Challenges and Future Research Directions on Data Computation
9.1 Introduction
9.2 Challenges and Future Research Directions for Big Data Analysis
9.2.1 Challenges
9.2.2 Future Research Directions
9.3 Challenges and Future Research Directions for IoT Data Analysis
9.4 Challenges and Future Research Directions for AR–VR Embedded Data Analysis
9.5 Challenges and Future Research Directions for Big Biological Data Analysis
9.6 Challenges and Future Research Directions for Quantum Data Analysis
9.7 Conclusion
References