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Big Data Applications in Industry 4.0 [Hardback]

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  • Formāts: Hardback, 422 pages, height x width: 234x156 mm, weight: 757 g, 45 Line drawings, black and white; 85 Halftones, black and white; 130 Illustrations, black and white
  • Izdošanas datums: 10-Feb-2022
  • Izdevniecība: Auerbach
  • ISBN-10: 1032008113
  • ISBN-13: 9781032008110
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Big Data Applications in Industry 4.0Palielināt
  • Formāts: Hardback, 422 pages, height x width: 234x156 mm, weight: 757 g, 45 Line drawings, black and white; 85 Halftones, black and white; 130 Illustrations, black and white
  • Izdošanas datums: 10-Feb-2022
  • Izdevniecība: Auerbach
  • ISBN-10: 1032008113
  • ISBN-13: 9781032008110
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781032008110.html
Keywords:
Industry 4.0 is the latest technological innovation in manufacturing with the goal to increase productivity in a flexible and efficient manner. Changing the way in which manufacturers operate, this revolutionary transformation is powered by various technology advances including Big Data analytics, Internet of Things (IoT), Artificial Intelligence (AI), and cloud computing. Big Data analytics has been identified as one of the significant components of Industry 4.0, as it provides valuable insights for smart factory management. Big Data and Industry 4.0 have the potential to reduce resource consumption and optimize processes, thereby playing a key role in achieving sustainable development.

Big Data Applications in Industry 4.0 covers the recent advancements that have emerged in the field of Big Data and its applications. The book introduces the concepts and advanced tools and technologies for representing and processing Big Data. It also covers applications of Big Data in such domains as financial services, education, healthcare, biomedical research, logistics, and warehouse management. Researchers, students, scientists, engineers, and statisticians can turn to this book to learn about concepts, technologies, and applications that solve real-world problems.

Features











An introduction to data science and the types of data analytics methods accessible today





An overview of data integration concepts, methodologies, and solutions





A general framework of forecasting principles and applications, as well as basic forecasting models including naļve, moving average, and exponential smoothing models





A detailed roadmap of the Big Data evolution and its related technological transformation in computing, along with a brief description of related terminologies





The application of Industry 4.0 and Big Data in the field of education





The features, prospects, and significant role of Big Data in the banking industry, as well as various use cases of Big Data in banking, finance services, and insurance





Implementing a Data Lake (DL) in the cloud and the significance of a data lake in decision making
Preface xiii
Acknowledgments xix
Editors xxi
Contributors xxiii
1 Data Science and Its Applications
1(38)
Paul Abraham
Lakshminarayanan S
1.1 Introduction to Data Science
2(8)
1.2 Data Science and Its Application in the Healthcare Industry
10(6)
1.3 Data Science and Its Application in the Retail and Retail E-Commerce
16(5)
1.4 Data Science and Its Application in the Banking, Financial Services and Insurance (BFSI) Sector
21(3)
1.5 Statistical Methods and Analytics Techniques Used across Businesses
24(1)
1.6 Statistical Methods and Analytics Techniques Used in Sales and Marketing
25(6)
1.7 Statistical Methods and Analytics Techniques Used in Supply Chain Management
31(3)
1.8 Statistical Methods and Analytics Techniques Used in Human Resource Management
34(3)
References
37(2)
2 Industry 4.0: Data and Data Integration
39(16)
Pavan Gundarapu
2.1 Introduction
40(1)
2.2 Data Integration
41(1)
2.3 Data Integration Solutions
41(5)
2.4 Data Integration Methodologies
46(5)
2.5 Service Providers
51(1)
2.6 Brief on Each Software
52(1)
2.7 Conclusion
53(1)
References
53(2)
3 Forecasting Principles and Models: An Overview
55(16)
R. Vijayaraghavan
3.1 Introduction
56(1)
3.2 Meaning of Forecasting
57(1)
3.3 Applications of Forecasting
57(2)
3.4 Limitations of Forecasting
59(1)
3.5 Types of Forecasting Procedures
60(3)
3.6 Process of Forecasting
63(1)
3.7 Basic Forecasting Models
64(4)
3.8 Software Tools for Forecasting
68(1)
3.9 Conclusions
68(2)
References
70(1)
4 Breaking Technology Barriers in Diabetes and Industry 4.0
71(14)
Krishnan Swaminathan
Thavamani D. Palaniswami
4.1 Brief Introduction to Diabetes
72(2)
4.2 "Big Data" Concept
74(2)
4.3 Recent Technological Advances in Diabetes Management
76(4)
4.4 Barriers in Diabetes Technology
80(1)
4.5 Technical Solutions to Break the Barriers
80(1)
4.6 Summary
81(1)
References
81(4)
5 Role of Big Data Analytics in Industrial Revolution 4.0
85(22)
V. Bhuvaneswari
5.1 Big Data Analytics
87(5)
5.2 Big Data Components
92(5)
5.3 Big Data & Industry 4.0
97(2)
5.4 Big Data Use Cases
99(3)
5.5 Big Data Roles
102(3)
References
105(2)
6 Big Data Infrastructure and Analytics for Education 4.0
107(18)
Chandra Eswaran
Rathinaraja Jeyaraj
6.1 Introduction
108(1)
6.2 Industrial Revolutions
108(1)
6.3 Advantages of Industry 4.0 in Education
109(2)
6.4 System for Smart Education
111(4)
6.5 Big Data Infrastructure for Smart Education
115(3)
6.6 Big Data Analysis for Smart Education
118(5)
6.7 Conclusion
123(1)
References
123(2)
7 Text Analytics in Big Data Environments
125(20)
R. Janani
S. Vijayarani
7.1 Introduction
126(1)
7.2 Text Analytics - Big Data Environment
127(10)
7.3 Applications of Text Analytics
137(2)
7.4 Issues and Research Challenges in Text Analytics
139(1)
7.5 Tools for Text Analytics
140(1)
7.6 Conclusion
141(1)
References
141(4)
8 Business Data Analytics: Applications and Research Trends
145(24)
S. Sharmila
S. Vijayarani
8.1 Big Data Analytics and Business Analytics: An Introduction
146(2)
8.2 Digital Revolution of Education 4.0
148(1)
8.3 Conceptual Framework of Big Data for Industry 4.0
149(4)
8.4 Business Analytics
153(7)
8.5 Applications of Big Data and Business Analytics
160(1)
8.6 Challenges of Big Data and Business Analytics
160(3)
8.7 Open Research Directions
163(1)
8.8 Conclusion
164(1)
References
164(5)
9 Role of Big Data Analytics in the Financial Service Sector
169(26)
V. Ramanujam
D. Napoleon
9.1 Introduction
171(1)
9.2 The Effect of Finance 4.0 in a Nutshell
172(3)
9.3 In The Banking Industry, Big Data
175(9)
9.4 Big Data Analytics in Finance Industry
184(3)
9.5 Sector of Finance Data Science
187(4)
9.6 Conclusion
191(1)
Acknowledgments
192(1)
References
192(3)
10 Role of Big Data Analytics in the Education Domain
195(22)
C. Sivamathi
S. Vijayarani
10.1 Introduction
196(6)
10.2 Need for Big Data Analytics in Education
202(2)
10.3 Applications of Big Data Analytics in Education
204(2)
10.4 Advantages of Big Data in Education
206(1)
10.5 Challenges in Implementing Big Data in Education
206(1)
10.6 Education 4.0 in India
207(1)
10.7 Case Study: Big Data Analytics in E-Learning
207(3)
10.8 Conclusion
210(2)
References
212(5)
11 Social Media Analytics
217(16)
E. Suganya
S. Vijayarani
11.1 Introduction
218(1)
11.2 Process of Social Media Analytics
219(2)
11.3 Social Media Analytics
221(3)
11.4 Techniques and Algorithms
224(2)
11.5 Tools
226(1)
11.6 Research Challenges
226(3)
11.7 Case Studies in Social Media Analytics
229(1)
11.8 Conclusion
230(1)
References
230(3)
12 Robust Statistics: Methods and Applications
233(26)
R. Muthukrishnan
12.1 Introduction
234(1)
12.2 History of Robust Statistics
235(1)
12.3 Classical Statistics vs. Robust Statistics
235(2)
12.4 Robust Statistical Measures
237(6)
12.5 Robust Regression Procedures
243(2)
12.6 Data Depth Procedures
245(3)
12.7 Statistical Learning
248(4)
12.8 Robust Statistics in R
252(1)
12.9 Summary
253(1)
References
254(5)
13 Big Data in Tribal Healthcare and Biomedical Research
259(38)
Dhivya Venkatesan
Abilash Valsala Gopalakrishnan
Narayanasamy Arul
Chhakchhuak Lalchhandama
Balachandar Vellingiri
N. Senthil Kumar
13.1 Introduction
260(4)
13.2 Data Lifecycle
264(3)
13.3 Big Data in Genomic Research
267(7)
13.4 Big Data in Biomedical Research
274(12)
13.5 Healthcare as a Big Data Repository
286(1)
13.6 Management of Big Data
286(2)
13.7 Challenges in Healthcare Data
288(2)
13.8 Tribal Research in India
290(1)
13.9 Conclusion
291(1)
Acknowledgments
291(1)
References
291(6)
14 PySpark towards Data Analytics
297(34)
J. Ramsingh
14.1 Introduction
299(3)
14.2 PySpark - SparkContext
302(3)
14.3 PySpark Shared Variables
305(1)
14.4 PySpark - RDD (Resilient Distributed Dataset)
305(10)
14.5 PySpark DataFrames
315(8)
14.6 PySpark MLlib (Machine Learning Libraries)
323(8)
15 How to Implement Data Lake for Large Enterprises
331(18)
Ragavendran Chandrasekaran
15.1 What Is a Data Warehouse?
332(2)
15.2 What Is a Data Lake?
334(1)
15.3 Why Do We Need Data Lake?
334(1)
15.4 Overview of Data Lake in Cloud
334(2)
15.5 Key Considerations for Data Lake Architecture
336(1)
15.6 Phases of Data Lake Implementation
337(4)
15.7 What to Load into Your Data Lake?
341(2)
15.8 A Cloud Data Lake Journey
343(5)
15.9 Conclusion
348(1)
References
348(1)
16 A Novel Application of Data Mining Techniques for Satellite Performance Analysis
349(18)
S.A. Kannan
T. Devi
16.1 Introduction
350(1)
16.2 Data Generation and Analysis
351(1)
16.3 Data Mining
352(1)
16.4 Artificial Satellites and Data Mining
353(1)
16.5 Statistical Techniques for Satellite Data Analysis
354(1)
16.6 Novel Application
355(1)
16.7 Selection of an Appropriate Data Mining Technique
356(3)
16.8 Satellite Telemetry Data - Association Mining
359(1)
16.9 Satellite Telemetry Data - Decision Tree Technique
360(1)
16.10 Satellite Telemetry Data - A Modified Brute-Force Rule-Induction Algorithm
361(1)
16.11 Methodology
362(1)
16.12 Conclusion
363(1)
References
364(3)
17 Big Data Analytics: A Text Mining Perspective and Applications in Biomedicine and Healthcare
367(42)
Jeyakumar Natarajan
Balu Bhasuran
Gurusamy Murugesan
17.1 Introduction
369(3)
17.2 Text Mining Overview and Related Fields
372(5)
17.3 Phases and Tasks of Text Mining
377(11)
17.4 Applications in Biomedicine
388(6)
17.5 Applications in Healthcare
394(7)
17.6 Conclusion
401(1)
References
401(8)
Index 409
Prof. P. Kaliraj is the Vice-Chancellor of Bharathiar University, Coimbatore, India.

Prof. T. Devi is the dean of faculty of research at the Department of Computer Applications, Bharathiar University, Coimbatore, India