Big Data Applications in Industry 4.0

✍ Scribed by P. Kaliraj, T. Devi

• Publisher: CRC Press
• Year: 2022
• Tongue: English
• Leaves: 447
• Edition: 1
• Category: Library

✦ Synopsis

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 artificial intelligence (AI), Big Data analytics, Internet-of-Things (IoT) 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.

The books 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 naïve, 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 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 for decision-making.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
Preface
Acknowledgments
Editors
Contributors
Chapter 1. Data Science and Its Applications
1.1 Introduction to Data Science
1.1.1 Data Science: A Definition
1.1.2 Data in the Business
1.1.3 Types of Data Analytics
1.1.4 Use Cases in the Business
1.1.5 Data Analytics Process, Implementation and Measurement
1.2 Data Science and Its Application in the Healthcare Industry
1.2.1 Data Types Generated in the Healthcare Sector
1.2.2 Analytics Use Cases in Healthcare
1.2.3 Future and Challenges
1.3 Data Science and Its Application in the Retail and Retail E-Commerce
1.3.1 Data Types Generated in the Retail and Retail E-Commerce Sector
1.3.2 Analytics Use Cases in Retail and Retail E-Commerce
1.3.3 Future and Challenges
1.4 Data Science and Its Application in the Banking, Financial Services and Insurance (BFSI) Sector
1.4.1 Data Types Generated in the BFSI Sector
1.4.2 Analytics Use Cases in BFSI
1.4.3 Future and Challenges
1.5 Statistical Methods and Analytics Techniques Used across Businesses
1.6 Statistical Methods and Analytics Techniques Used in Sales and Marketing
1.6.1 Data Types Generated in Sales and Marketing Function
1.6.2 Statistical Methods and Analytical Techniques
1.6.3 Future and Challenges
1.7 Statistical Methods and Analytics Techniques Used in Supply Chain Management
1.7.1 Data Types Used in the SCM
1.7.2 Analytics Use Cases in SCM
1.7.3 Future and Challenges
1.8 Statistical Methods and Analytics Techniques Used in Human Resource Management
1.8.1 Data Types Generated in Human Resource Management
1.8.2 Analytics Use Cases in Human Resource Management
1.8.3 Future and Challenges
References
Chapter 2. Industry 4.0: Data and Data Integration
2.1 Introduction
2.2 Data Integration
2.3 Data Integration Solutions
2.3.1 Custom Code
2.3.2 ETL
2.3.2.1 Extract
2.3.2.2 Transform
2.3.2.3 Load
2.3.3 ELT
2.4 Data Integration Methodologies
2.4.1 Bulk Loading
2.4.2 Daily Differentials
2.4.3 Insert Only
2.4.4 Database Replication
2.4.5 Batch Processing
2.4.6 Streaming
2.5 Service Providers
2.6 Brief on Each Software
2.7 Conclusion
References
Chapter 3. Forecasting Principles and Models: An Overview
3.1 Introduction
3.2 Meaning of Forecasting
3.3 Applications of Forecasting
3.3.1 Business Forecasting
3.3.2 Forecasting in Supply Chain Management
3.3.3 Epidemiological Forecasting
3.3.4 Weather Forecasting
3.4 Limitations of Forecasting
3.5 Types of Forecasting Procedures
3.5.1 Qualitative Approach
3.5.2 Quantitative Approach
3.6 Process of Forecasting
3.6.1 Problem Identification
3.6.2 Collection of Data
3.6.3 Description and Manipulation of Data
3.6.4 Analysis of Data, Model Construction, and Evaluation
3.6.5 Model Implementation, Forecast Evaluation, and Model Performance
3.7 Basic Forecasting Models
3.7.1 Naïve Forecast Model
3.7.2 Forecasting with Averaging Models
3.7.2.1 Simple Averages
3.7.2.2 Moving Averages
3.7.3 Exponential Smoothing Models
3.8 Software Tools for Forecasting
3.9 Conclusions
References
Chapter 4. Breaking Technology Barriers in Diabetes and Industry 4.0
4.1 Brief Introduction to Diabetes
4.1.1 The Epidemic of Diabetes
4.1.2 Burden of Type 1 Diabetes in India
4.1.3 Burden of Type 2 Diabetes in India
4.1.4 Burden of Type 1, Type 2 Diabetes and Prediabetes in India: So, What?
4.2 "Big Data" Concept
4.2.1 "Big Data": Definition and Concepts
4.2.2 Big Data and Diabetes
4.2.3 Big Data, Predictive Analysis and Diabetes
4.2.4 Case Study in Big Data
4.3 Recent Technological Advances in Diabetes Management
4.3.1 Closed-Loop Insulin Pump Systems
4.3.2 Glucose Monitoring Sensors
4.3.3 Smartwatches for Noninvasive Glucose Monitoring
4.3.4 Deep Machine Learning for Diabetic Retinopathy Screening
4.4 Barriers in Diabetes Technology
4.5 Technical Solutions to Break the Barriers
4.6 Summary
References
Chapter 5. Role of Big Data Analytics in Industrial Revolution 4.0
5.1 Big Data Analytics
5.1.1 Data: Terminologies
5.1.2 Data Evolution: A Look-Back
5.1.2.1 Transformation: Data to Big Data
5.1.2.2 Data Formats and Sources: Data Growth
5.1.3 Big Data: A Comprehensive View
5.1.3.1 Definition
5.1.3.2 Data Analysis
5.1.3.2.1 Data Analysis
5.1.3.2.2 Data Analytics
5.1.3.3 Big Data vs. Statistics vs. Data Mining
5.1.3.3.1 Data Science
5.2 Big Data Components
5.2.1 Big Data Characteristics
5.2.1.1 Big Data Myths
5.2.2 Big Data Processing: Architecture
5.2.2.1 Traditional vs. Big Data Framework
5.2.3 Big Data-Related Technologies
5.2.4 Big Data: Industry 4.0 Applications
5.3 Big Data & Industry 4.0
5.3.1 Big Data Analytics: Essentials
5.3.2 Data Migration to Cloud
5.3.3 Predictive Analytics
5.3.4 Artificial Intelligence
5.4 Big Data Use Cases
5.4.1 Big Data Use Case: Social Good
5.4.1.1 An Epidemic: Preventive Care Management
5.4.1.2 Natural Resource Management: Oil and Gas
5.4.1.3 Agriculture
5.4.2 Big Data: Industry Use Case
5.4.2.1 Warehouse Management and Supply Chain
5.4.2.2 Automobile Industry
5.4.2.3 Pharmaceuticals
5.4.2.4 Sports Analytics
5.5 Big Data Roles
5.5.1 Data Scientist
5.5.2 Big Data Engineer
5.5.3 Machine Learning Engineer
5.5.4 Data Analyst
5.5.5 Business Analyst
5.5.6 Statisticians
References
Chapter 6. Big Data Infrastructure and Analytics for Education 4.0
6.1 Introduction
6.2 Industrial Revolutions
6.3 Advantages of Industry 4.0 in Education
6.4 System for Smart Education
6.4.1 Stakeholders
6.4.2 Dashboard
6.4.3 Internet of Things
6.4.4 Cloud Computing
6.4.5 AI in Smart Education
6.4.6 Augmented Reality
6.5 Big Data Infrastructure for Smart Education
6.5.1 Database and Distributed File System
6.5.2 Stream Processing
6.5.3 Batch Processing
6.5.4 Data Visualization
6.5.5 Data Processing Model
6.6 Big Data Analysis for Smart Education
6.6.1 Data Science
6.6.2 Data Analyst
6.6.3 Big Data Analytics
6.6.4 Text Analytics
6.6.5 Text Summarization
6.6.6 Question Answering (QA)
6.6.7 Sentiment Analysis (Opinion Mining)
6.6.8 Audio Analytics
6.6.9 Video Analytics
6.6.10 Social Media Analytics
6.7 Conclusion
References
Chapter 7. Text Analytics in Big Data Environments
7.1 Introduction
7.1.1 Need for Text Analytics
7.2 Text Analytics: Big Data Environment
7.2.1 Text Data Collection
7.2.1.1 Data Collection Methods
7.2.2 Data Storage
7.2.3 Text Preprocessing
7.2.4 Text Analysis
7.2.4.1 Text Classification
7.2.4.2 Text Clustering
7.2.4.3 Text Summarization
7.2.4.4 Sentimental Analysis
7.2.4.5 Topic Modeling
7.2.5 Result Interpretation
7.2.6 Visualization
7.3 Applications of Text Analytics
7.4 Issues and Research Challenges in Text Analytics
7.5 Tools for Text Analytics
7.6 Conclusion
References
Chapter 8. Business Data Analytics: Applications and Research Trends
8.1 Big Data Analytics and Business Analytics: An Introduction
8.2 Digital Revolution of Education 4.0
8.2.1 Education 4.0
8.2.2 Requirement of Education 4.0 in Industry
8.2.3 Benefits of Education 4.0 for Business Sector
8.2.4 Influence of Industrial Revolution 4.0 on Higher Education
8.3 Conceptual Framework of Big Data for Industry 4.0
8.3.1 Big Data Application Design
8.3.2 Preprocessing Input Data Streams
8.3.3 Distributed Infrastructure
8.3.4 Distribution of Results
8.4 Business Analytics
8.4.1 Business Analytics vs. Business Intelligence
8.4.1.1 Business Analytics (BA)
8.4.1.2 Business Intelligence (BI)
8.5 Applications of Big Data and Business Analytics
8.6 Challenges of Big Data and Business Analytics
8.6.1 Uncertainty of Data Management
8.6.2 Talent Gap
8.6.3 Synchronising the Data Sources
8.6.4 Issues with Data Integration
8.7 Open Research Directions
8.8 Conclusion
References
Chapter 9. Role of Big Data Analytics in the Financial Service Sector
9.1 Introduction
9.2 The Effect of Finance 4.0 in a Nutshell
9.2.1 Data Revolution
9.2.2 What Does Finance 4.0 Mean?
9.2.3 The Revolution of Finance Industry
9.2.4 Embrace Industry 4.0 in Finance Industry
9.2.5 Banking and Big Data
9.2.5.1 Easy to Customer Segment Identification
9.2.5.2 Adopt the Customized Familiarity
9.2.5.3 Client Behavioral Approach
9.2.5.4 Profit-Sharing Possibilities
9.2.5.5 Deduction of Deceitful Performance
9.3 Big Data in the Banking Industry
9.3.1 Four V's of Big Data
9.3.2 Arrangement of Big Data
9.3.3 Big Data Analysis in Banking
9.3.4 Leveraging Big Data Analysis
9.3.4.1 Improved Deception Revealing
9.3.4.2 Greater Risk Appraisal
9.3.4.3 Enlarged Customer Continued Possession
9.3.4.4 Service or Product Individuality
9.3.4.5 Efficient Client Criticism
9.3.5 Significant Role of Big Data in Banking and Finance
9.3.6 Prospect of Big Data in Finance Sector
9.3.7 The Banking Industry's Big Data Analytics Potential
9.3.7.1 Preventing Frauds
9.3.7.2 Identifying and Acquiring Customers
9.3.7.3 Retaining Customers
9.3.7.4 Enhancing Customer Experience
9.3.7.5 Optimizing Operations
9.3.7.6 Meeting Regulatory Requirements and Dealing with Setbacks in Real Time
9.3.7.7 Optimizing the Overall Product Portfolio/Improving Product Design
9.3.7.8 Increasing Transparency
9.3.8 Advantages of Big Data in Financial Sectors
9.3.8.1 Identification of Innovative Services
9.3.8.2 Minimize the Deception Movement
9.3.8.3 Enhanced Maneuvers
9.3.8.4 Improved Operations
9.3.8.5 Identifying and Analyzing Potential Issue
9.3.8.6 A Greater Understanding of Market Conditions
9.3.8.7 Better Customer Service
9.3.8.8 Endeavour to Accomplish a High Growth
9.3.8.9 Marketing Plan and Tactics
9.3.8.10 Designed Constructive Approach in Decrease Costs
9.4 Big Data Analytics in Finance Industry
9.4.1 Finance Analysis in Cloud
9.4.2 Finance Team Needs Big Data Experts: How to Find Them
9.4.3 Data Science in Banking and Finance
9.4.4 Big Data Analysis to Improve Finance Industry
9.4.5 Big Data Analysis in Finance: Pros and Cons
9.5 Sector of Finance Data Science
9.5.1 Data Science for the Internet Age
9.5.2 Modernize Data Science in Finance Industry
9.5.3 The Financial Sector Needs Data Science
9.5.4 Machine Learning in Finance Information
9.5.5 Sentiment Analysis in Finance or Service Sector
9.5.6 Predictive Analytics in Service or Finance Sector
9.5.7 Social Media Insights for Finance Industry
9.5.8 Analytics Tools for Finance Data
9.5.9 Finance Sector and Its Upcoming Role of Data Analysis
9.6 Conclusion
Acknowledgments
References
Chapter 10. Role of Big Data Analytics in the Education Domain
10.1 Introduction
10.1.1 Industry 4.0
10.1.1.1 First Industrial Revolution (IR 1.0)
10.1.1.2 Second Industrial Revolution (IR 2.0)
10.1.1.3 Third Industrial Revolution (IR 3.0)
10.1.1.4 Fourth Industrial Revolution (IR 4.0)
10.1.2 Revolution of Education
10.1.3 Education 4.0
10.1.3.1 5 I's of Learning in Education 4.0
10.1.4 Big Data Analytics
10.2 Need for Big Data Analytics in Education
10.2.1 Learning Analytics
10.2.2 Predictive Analytics
10.2.3 Academic Analytics
10.2.4 Text Analytics
10.2.5 Visual Analytics
10.3 Applications of Big Data Analytics in Education
10.3.1 Creating Predictive Model
10.3.2 Personalized Curriculum
10.3.3 Adaptive Learning
10.3.4 Personalized Resources
10.3.5 Data-Driven Decision-Making Culture
10.3.6 Access Data Easier
10.3.7 Virtual Interview
10.3.8 Design a New Course
10.4 Advantages of Big Data in Education
10.5 Challenges in Implementing Big Data in Education
10.6 Education 4.0 in India
10.7 Case Study: Big Data Analytics in E-Learning
10.7.1 E-Learning Platforms
10.8 Conclusion
References
Chapter 11. Social Media Analytics
11.1 Introduction
11.2 Process of Social Media Analytics
11.2.1 Capture Data
11.2.2 Understand Data
11.2.3 Present Data
11.3 Social Media Analytics
11.3.1 Content Analysis
11.3.1.1 Topic Identification
11.3.1.2 Sentiment Analysis
11.3.1.3 Social Multimedia Analysis
11.3.2 Group and Network Analysis
11.3.2.1 Group Identification
11.3.2.2 Relationship Characterization
11.3.3 Prediction
11.4 Techniques and Algorithms
11.4.1 Techniques
11.4.1.1 NLP
11.4.1.2 News Analytics
11.4.1.3 Opinion Mining
11.4.1.4 Scraping
11.4.1.5 Text Analytics
11.4.2 Machine Learning and Deep Learning Algorithms
11.4.2.1 Artificial Neural Network (ANN)
11.4.2.2 SVM
11.4.2.3 Convolutional Neural Network (CNN)
11.4.2.4 Recurrent Neural Network (RNN)
11.4.2.5 Auto-Encoder (AE)
11.4.2.6 Deep Belief Network (DBN)
11.5 Tools
11.6 Research Challenges
11.7 Case Studies in Social Media Analytics
11.7.1 Barclays
11.7.2 Keen
11.7.3 Samsung
11.7.4 TOMS Shoes
11.7.5 Yale
11.7.6 Cisco
11.7.7 Kmart
11.8 Conclusion
References
Chapter 12. Robust Statistics: Methods and Applications
12.1 Introduction
12.2 History of Robust Statistics
12.3 Classical Statistics vs. Robust Statistics
12.4 Robust Statistical Measures
12.5 Robust Regression Procedures
12.6 Data Depth Procedures
12.7 Statistical Learning
12.7.1 Principal Component Analysis
12.7.2 Factor Analysis
12.7.3 Discriminant Analysis
12.7.4 Cluster Analysis
12.8 Robust Statistics in R
12.9 Summary
References
Chapter 13. Big Data in Tribal Healthcare and Biomedical Research
13.1 Introduction
13.1.1 Photographs Were Taken During the NRDMS Project Data Survey
13.1.2 NRDMS Project-Based Website (www.nrdms-bu.edu.in)
13.1.3 Big Data Approaches
13.2 Data Lifecycle
13.2.1 Big Data in Socioeconomic Status
13.3 Big Data in Genomic Research
13.3.1 Hadoop
13.3.2 Apache Spark
13.3.3 NGS Read Alignment
13.3.4 Variation Calling
13.3.5 Variant Annotation
13.3.6 Metagenomics
13.4 Big Data in Biomedical Research
13.5 Healthcare as a Big Data Repository
13.5.1 Electronic Health Records (EHR)
13.5.2 Digital Information about Healthcare and Big Data
13.6 Management of Big Data
13.7 Challenges in Healthcare Data
13.7.1 Storage
13.7.2 Data Cleansing
13.7.3 Combined Format
13.7.4 Accuracy
13.7.5 Image Preprocessing
13.7.6 Security
13.7.7 Metadata
13.7.8 Querying
13.7.9 Visualization
13.7.10 Data Distribution
13.8 Tribal Research in India
13.8.1 Indigenous Data
13.9 Conclusion
13.9.1 Priorities
Acknowledgments
References
Chapter 14. PySpark toward Data Analytics
14.1 Introduction
14.1.1 Apache Spark
14.1.1.1 Spark Architecture
I Resilient Distributed Datasets (RDD)
II Directed Acyclic Graph (DAG)
III Spark Context (SC)
14.1.2 PySpark
14.1.2.1 Prerequisites to PySpark
14.1.2.2 PySpark - Environment Setup
14.2 PySpark: SparkContext
14.2.1 SparkContext Parameters
14.2.2 SparkContext Example
14.3 PySpark Shared Variables
14.3.1 Broadcast Variables
14.3.1 Accumulators
14.4 PySpark: RDD (Resilient Distributed Dataset)
14.4.1 Transformations
14.4.2 Actions
14.4.3 Features of PySpark RDDs
14.4.3.1 In-Memory Computations
14.4.3.2 Lazy Evaluation
14.4.3.3 Fault-Tolerant
14.4.3.4 Immutability
14.4.3.5 Partitioning
14.4.3.6 Persistence
14.4.3.7 Coarse-Grained Operations
14.4.4 Creating RDD
14.4.3 Operations in RDD
14.5 PySpark DataFrames
14.5.1 Need of DataFrames
14.5.1.1 Processing Heterogeneous Data
14.5.1.2 Slicing and Dicing
14.5.2 Features of DataFrame
14.5.3 PySpark DataFrames
14.5.4 Creating DataFrame from RDD
14.5.5 Creating the DataFrame from CSV, JSON and Text Files
14.5.6 DataFrame Manipulations
14.5.6.1 How to Retrieve the Datatype of Columns in Our Dataset?
14.5.6.2 How to View the First n Observation?
14.5.6.3 How to Get the Statistics Summary of Numerical Columns in a DataFrame (Standard Deviance, Mean, Max, Count, Min)?
14.5.6.4 How to Find the Distinct Data and Remove Duplicate Values?
14.5.6.5 Removing and Filling Null Values from Data Frames
14.6 PySpark MLlib (Machine Learning Libraries)
14.6.1 Various Tools Provided by MLlib
14.6.1.1 Why PySpark MLlib
14.6.2 PySpark MLlib Algorithms
14.6.2.1 Classification Using PySpark MLlib
14.6.2.2 Logistic Regression
14.6.2.3 Logistic Regression Using Logistic Regression With LBFGS
14.6.2.4 Collaborative Filtering
14.6.2.5 Rating Class in pyspark.mllib.recommendation
14.6.2.6 Alternating Least Squares (ALS)
14.6.2.7 Model Evaluation Using MSE
14.6.2.8 Clustering
Chapter 15. How to Implement Data Lake for Large Enterprises
15.1 What Is a Data Warehouse?
15.1.1 Roles of Data Warehouse for Industries
15.2 What Is a Data Lake?
15.3 Why Do We Need Data Lake?
15.4 Overview of Data Lake in Cloud
15.5 Key Considerations for Data Lake Architecture
15.6 Phases of Data Lake Implementation
15.6.1 Data Lake Architecture on Amazon Web Services
15.6.2 Data Lake Architecture on Google Cloud Platform
15.6.3 Azure Cloud Data Lake
15.7 What to Load into Your Data Lake?
15.8 A Cloud Data Lake Journey
15.8.1 Cloud Infrastructures
15.8.2 Data Lake Storage
15.8.3 Data Transformation
15.8.4 Data Security
15.9 Conclusion
References
Chapter 16. A Novel Application of Data Mining Techniques for Satellite Performance Analysis
16.1 Introduction
16.2 Data Generation and Analysis
16.3 Data Mining
16.4 Artificial Satellites and Data Mining
16.5 Statistical Techniques for Satellite Data Analysis
16.6 Novel Application
16.7 Selection of an Appropriate Data Mining Technique
16.8 Satellite Telemetry Data: Association Mining
16.9 Satellite Telemetry Data: Decision Tree Technique
16.10 Satellite Telemetry Data: A Modified Brute-Force Rule-Induction Algorithm
16.11 Methodology
16.12 Conclusion
References
Chapter 17. Big Data Analytics: A Text Mining Perspective and Applications in Biomedicine and Healthcare
17.1 Introduction
17.1.1 Big Data
17.1.2 Text Mining
17.1.3 Applications in Biomedicine and Healthcare
17.2 Text Mining Overview and Related Fields
17.2.1 Definition and Overview
17.2.2 Data Mining
17.2.3 Natural Language Processing
17.2.4 Machine Learning
17.2.5 Text Mining: Big Picture
17.3 Phases and Tasks of Text Mining
17.3.1 Information Retrieval
17.3.1.1 IR and Big Data
17.3.1.2 Big Data File System Architecture
17.3.2 Information Extraction
17.3.2.1 Named Entity Recognition
17.3.2.2 Relation Extraction
17.3.2.3 Event Extraction
17.3.2.4 IE and Big Data
17.3.3 Knowledge Discovery and Hypothesis Generation
17.3.3.1 TM and Big Data
17.4 Applications in Biomedicine
17.4.1 Biomedical Text Mining
17.4.2 Biomedical Text Mining Resources
17.4.3 Bio-Named Entity Recognition
17.4.4 Bio-Relation Extraction
17.4.5 Event Extraction
17.4.6 Applications
17.4.7 Case Studies in Cancer Literature
17.5 Applications in Healthcare
17.5.1 Healthcare Text Mining
17.5.2 Electronic Health Records Mining
17.5.3 Health-Related Social Media Mining
17.6 Conclusion
References
Index