Details

Machine Learning Approach for Cloud Data Analytics in IoT

1. Aufl.

von: Sachi Nandan Mohanty, Jyotir Moy Chatterjee, Monika Mangla, Suneeta Satpathy, Sirisha Potluri
190,99 €
Verlag:	Wiley
Format:	EPUB
Veröffentl.:	14.07.2021
ISBN/EAN:	9781119785859
Sprache:	englisch
Anzahl Seiten:	528

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Titelbeschreibung

Machine Learning Approach for Cloud Data Analytics in IoT The book covers the multidimensional perspective of machine learning through the perspective of cloud computing and Internet of Things ranging from fundamentals to advanced applications Sustainable computing paradigms like cloud and fog are capable of handling issues related to performance, storage and processing, maintenance, security, efficiency, integration, cost, energy and latency in an expeditious manner. In order to expedite decision-making involved in the complex computation and processing of collected data, IoT devices are connected to the cloud or fog environment. Since machine learning as a service provides the best support in business intelligence, organizations have been making significant investments in this technology. Machine Learning Approach for Cloud Data Analytics in IoT elucidates some of the best practices and their respective outcomes in cloud and fog computing environments. It focuses on all the various research issues related to big data storage and analysis, large-scale data processing, knowledge discovery and knowledge management, computational intelligence, data security and privacy, data representation and visualization, and data analytics. The featured technologies presented in the book optimizes various industry processes using business intelligence in engineering and technology. Light is also shed on cloud-based embedded software development practices to integrate complex machines so as to increase productivity and reduce operational costs. The various practices of data science and analytics which are used in all sectors to understand big data and analyze massive data patterns are also detailed in the book.

Inhaltsverzeichnis

Preface xix Acknowledgment xxiii 1 Machine Learning–Based Data Analysis 1 M. Deepika and K. Kalaiselvi 1.1 Introduction 1 1.2 Machine Learning for the Internet of Things Using Data Analysis 4 1.2.1 Computing Framework 6 1.2.2 Fog Computing 6 1.2.3 Edge Computing 6 1.2.4 Cloud Computing 7 1.2.5 Distributed Computing 7 1.3 Machine Learning Applied to Data Analysis 7 1.3.1 Supervised Learning Systems 8 1.3.2 Decision Trees 9 1.3.3 Decision Tree Types 9 1.3.4 Unsupervised Machine Learning 10 1.3.5 Association Rule Learning 10 1.3.6 Reinforcement Learning 10 1.4 Practical Issues in Machine Learning 11 1.5 Data Acquisition 12 1.6 Understanding the Data Formats Used in Data Analysis Applications 13 1.7 Data Cleaning 14 1.8 Data Visualization 15 1.9 Understanding the Data Analysis Problem-Solving Approach 15 1.10 Visualizing Data to Enhance Understanding and Using Neural Networks in Data Analysis 16 1.11 Statistical Data Analysis Techniques 17 1.11.1 Hypothesis Testing 18 1.11.2 Regression Analysis 18 1.12 Text Analysis and Visual and Audio Analysis 18 1.13 Mathematical and Parallel Techniques for Data Analysis 19 1.13.1 Using Map-Reduce 20 1.13.2 Leaning Analysis 20 1.13.3 Market Basket Analysis 21 1.14 Conclusion 21 References 22 2 Machine Learning for Cyber-Immune IoT Applications 25 Suchismita Sahoo and Sushree Sangita Sahoo 2.1 Introduction 25 2.2 Some Associated Impactful Terms 27 2.2.1 IoT 27 2.2.2 IoT Device 28 2.2.3 IoT Service 29 2.2.4 Internet Security 29 2.2.5 Data Security 30 2.2.6 Cyberthreats 31 2.2.7 Cyber Attack 31 2.2.8 Malware 32 2.2.9 Phishing 32 2.2.10 Ransomware 33 2.2.11 Spear-Phishing 33 2.2.12 Spyware 34 2.2.13 Cybercrime 34 2.2.14 IoT Cyber Security 35 2.2.15 IP Address 36 2.3 Cloud Rationality Representation 36 2.3.1 Cloud 36 2.3.2 Cloud Data 37 2.3.3 Cloud Security 38 2.3.4 Cloud Computing 38 2.4 Integration of IoT With Cloud 40 2.5 The Concepts That Rules Over 41 2.5.1 Artificial Intelligent 41 2.5.2 Overview of Machine Learning 41 2.5.2.1 Supervised Learning 41 2.5.2.2 Unsupervised Learning 42 2.5.3 Applications of Machine Learning in Cyber Security 43 2.5.4 Applications of Machine Learning in Cybercrime 43 2.5.5 Adherence of Machine Learning With Cyber Security in Relevance to IoT 43 2.5.6 Distributed Denial-of-Service 44 2.6 Related Work 45 2.7 Methodology 46 2.8 Discussions and Implications 48 2.9 Conclusion 49 References 49 3 Employing Machine Learning Approaches for Predictive Data Analytics in Retail Industry 53 Rakhi Akhare, Sanjivani Deokar, Monika Mangla and Hardik Deshmukh 3.1 Introduction 53 3.2 Related Work 55 3.3 Predictive Data Analytics in Retail 56 3.3.1 ML for Predictive Data Analytics 58 3.3.2 Use Cases 59 3.3.3 Limitations and Challenges 61 3.4 Proposed Model 61 3.4.1 Case Study 63 3.5 Conclusion and Future Scope 68 References 69 4 Emerging Cloud Computing Trends for Business Transformation 71 Prasanta Kumar Mahapatra, Alok Ranjan Tripathy and Alakananda Tripathy 4.1 Introduction 71 4.1.1 Computing Definition Cloud 72 4.1.2 Advantages of Cloud Computing Over On-Premises IT Operation 73 4.1.3 Limitations of Cloud Computing 74 4.2 History of Cloud Computing 74 4.3 Core Attributes of Cloud Computing 75 4.4 Cloud Computing Models 77 4.4.1 Cloud Deployment Model 77 4.4.2 Cloud Service Model 79 4.5 Core Components of Cloud Computing Architecture: Hardware and Software 83 4.6 Factors Need to Consider for Cloud Adoption 84 4.6.1 Evaluating Cloud Infrastructure 84 4.6.2 Evaluating Cloud Provider 85 4.6.3 Evaluating Cloud Security 86 4.6.4 Evaluating Cloud Services 86 4.6.5 Evaluating Cloud Service Level Agreements (SLA) 87 4.6.6 Limitations to Cloud Adoption 87 4.7 Transforming Business Through Cloud 88 4.8 Key Emerging Trends in Cloud Computing 89 4.8.1 Technology Trends 90 4.8.2 Business Models 92 4.8.3 Product Transformation 92 4.8.4 Customer Engagement 92 4.8.5 Employee Empowerment 93 4.8.6 Data Management and Assurance 93 4.8.7 Digitalization 93 4.8.8 Building Intelligence Cloud System 93 4.8.9 Creating Hyper-Converged Infrastructure 94 4.9 Case Study: Moving Data Warehouse to Cloud Boosts Performance for Johnson & Johnson 94 4.10 Conclusion 95 References 96 5 Security of Sensitive Data in Cloud Computing 99 Kirti Wanjale, Monika Mangla and Paritosh Marathe 5.1 Introduction 100 5.1.1 Characteristics of Cloud Computing 100 5.1.2 Deployment Models for Cloud Services 101 5.1.3 Types of Cloud Delivery Models 102 5.2 Data in Cloud 102 5.2.1 Data Life Cycle 103 5.3 Security Challenges in Cloud Computing for Data 105 5.3.1 Security Challenges Related to Data at Rest 106 5.3.2 Security Challenges Related to Data in Use 107 5.3.3 Security Challenges Related to Data in Transit 107 5.4 Cross-Cutting Issues Related to Network in Cloud 108 5.5 Protection of Data 109 5.6 Tighter IAM Controls 114 5.7 Conclusion and Future Scope 117 References 117 6 Cloud Cryptography for Cloud Data Analytics in IoT 119 N. Jayashri and K. Kalaiselvi 6.1 Introduction 120 6.2 Cloud Computing Software Security Fundamentals 120 6.3 Security Management 122 6.4 Cryptography Algorithms 123 6.4.1 Types of Cryptography 123 6.5 Secure Communications 127 6.6 Identity Management and Access Control 133 6.7 Autonomic Security 137 6.8 Conclusion 139 References 139 7 Issues and Challenges of Classical Cryptography in Cloud Computing 143 Amrutanshu Panigrahi, Ajit Kumar Nayak and Rourab Paul 7.1 Introduction 144 7.1.1 Problem Statement and Motivation 145 7.1.2 Contribution 146 7.2 Cryptography 146 7.2.1 Cryptography Classification 147 7.2.1.1 Classical Cryptography 147 7.2.1.2 Homomorphic Encryption 149 7.3 Security in Cloud Computing 150 7.3.1 The Need for Security in Cloud Computing 151 7.3.2 Challenges in Cloud Computing Security 152 7.3.3 Benefits of Cloud Computing Security 153 7.3.4 Literature Survey 154 7.4 Classical Cryptography for Cloud Computing 157 7.4.1 RSA 157 7.4.2 AES 157 7.4.3 DES 158 7.4.4 Blowfish 158 7.5 Homomorphic Cryptosystem 158 7.5.1 Paillier Cryptosystem 159 7.5.1.1 Additive Homomorphic Property 159 7.5.2 RSA Homomorphic Cryptosystem 160 7.5.2.1 Multiplicative Homomorphic Property 160 7.6 Implementation 160 7.7 Conclusion and Future Scope 162 References 162 8 Cloud-Based Data Analytics for Monitoring Smart Environments 167 D. Karthika 8.1 Introduction 167 8.2 Environmental Monitoring for Smart Buildings 169 8.2.1 Smart Environments 169 8.3 Smart Health 171 8.3.1 Description of Solutions in General 171 8.3.2 Detection of Distress 172 8.3.3 Green Protection 173 8.3.4 Medical Preventive/Help 174 8.4 Digital Network 5G and Broadband Networks 174 8.4.1 IoT-Based Smart Grid Technologies 174 8.5 Emergent Smart Cities Communication Networks 175 8.5.1 RFID Technologies 177 8.5.2 Identifier Schemes 177 8.6 Smart City IoT Platforms Analysis System 177 8.7 Smart Management of Car Parking in Smart Cities 178 8.8 Smart City Systems and Services Securing: A Risk-Based Analytical Approach 178 8.9 Virtual Integrated Storage System 179 8.10 Convolutional Neural Network (CNN) 181 8.10.1 IEEE 802.15.4 182 8.10.2 BLE 182 8.10.3 ITU-T G.9959 (Z-Wave) 183 8.10.4 NFC 183 8.10.5 LoRaWAN 184 8.10.6 Sigfox 184 8.10.7 NB-IoT 184 8.10.8 PLC 184 8.10.9 MS/TP 184 8.11 Challenges and Issues 185 8.11.1 Interoperability and Standardization 185 8.11.2 Customization and Adaptation 186 8.11.3 Entity Identification and Virtualization 187 8.11.4 Big Data Issue in Smart Environments 187 8.12 Future Trends and Research Directions in Big Data Platforms for the Internet of Things 188 8.13 Case Study 189 8.14 Conclusion 191 References 191 9 Performance Metrics for Comparison of Heuristics Task Scheduling Algorithms in Cloud Computing Platform 195 Nidhi Rajak and Ranjit Rajak 9.1 Introduction 195 9.2 Workflow Model 197 9.3 System Computing Model 198 9.4 Major Objective of Scheduling 198 9.5 Task Computational Attributes for Scheduling 198 9.6 Performance Metrics 200 9.7 Heuristic Task Scheduling Algorithms 201 9.7.1 Heterogeneous Earliest Finish Time (HEFT) Algorithm 202 9.7.2 Critical-Path-on-a-Processor (CPOP) Algorithm 208 9.7.3 As Late As Possible (ALAP) Algorithm 213 9.7.4 Performance Effective Task Scheduling (PETS) Algorithm 217 9.8 Performance Analysis and Results 220 9.9 Conclusion 224 References 224 10 Smart Environment Monitoring Models Using Cloud-Based Data Analytics: A Comprehensive Study 227 Pradnya S. Borkar and Reena Thakur 10.1 Introduction 228 10.1.1 Internet of Things 229 10.1.2 Cloud Computing 230 10.1.3 Environmental Monitoring 232 10.2 Background and Motivation 234 10.2.1 Challenges and Issues 234 10.2.2 Technologies Used for Designing Cloud-Based Data Analytics 240 10.2.2.1 Communication Technologies 241 10.2.3 Cloud-Based Data Analysis Techniques and Models 243 10.2.3.1 MapReduce for Data Analysis 243 10.2.3.2 Data Analysis Workflows 246 10.2.3.3 NoSQL Models 247 10.2.4 Data Mining Techniques 248 10.2.5 Machine Learning 251 10.2.5.1 Significant Importance of Machine Learning and Its Algorithms 253 10.2.6 Applications 253 10.3 Conclusion 261 References 262 11 Advancement of Machine Learning and Cloud Computing in the Field of Smart Health Care 273 Aradhana Behura, Shibani Sahu and Manas Ranjan Kabat 11.1 Introduction 274 11.2 Survey on Architectural WBAN 278 11.3 Suggested Strategies 280 11.3.1 System Overview 280 11.3.2 Motivation 281 11.3.3 DSCB Protocol 281 11.3.3.1 Network Topology 282 11.3.3.2 Starting Stage 282 11.3.3.3 Cluster Evolution 282 11.3.3.4 Sensed Information Stage 283 11.3.3.5 Choice of Forwarder Stage 283 11.3.3.6 Energy Consumption as Well as Routing Stage 285 11.4 CNN-Based Image Segmentation (UNet Model) 287 11.5 Emerging Trends in IoT Healthcare 290 11.6 Tier Health IoT Model 294 11.7 Role of IoT in Big Data Analytics 294 11.8 Tier Wireless Body Area Network Architecture 296 11.9 Conclusion 303 References 303 12 Study on Green Cloud Computing—A Review 307 Meenal Agrawal and Ankita Jain 12.1 Introduction 307 12.2 Cloud Computing 308 12.2.1 Cloud Computing: On-Request Outsourcing-Pay-as-You-Go 308 12.3 Features of Cloud Computing 309 12.4 Green Computing 309 12.5 Green Cloud Computing 309 12.6 Models of Cloud Computing 310 12.7 Models of Cloud Services 310 12.8 Cloud Deployment Models 311 12.9 Green Cloud Architecture 312 12.10 Cloud Service Providers 312 12.11 Features of Green Cloud Computing 313 12.12 Advantages of Green Cloud Computing 313 12.13 Limitations of Green Cloud Computing 314 12.14 Cloud and Sustainability Environmental 315 12.15 Statistics Related to Cloud Data Centers 315 12.16 The Impact of Data Centers on Environment 315 12.17 Virtualization Technologies 316 12.18 Literature Review 316 12.19 The Main Objective 318 12.20 Research Gap 319 12.21 Research Methodology 319 12.22 Conclusion and Suggestions 320 12.23 Scope for Further Research 320 References 321 13 Intelligent Reclamation of Plantae Affliction Disease 323 Reshma Banu, G.F Ali Ahammed and Ayesha Taranum 13.1 Introduction 324 13.2 Existing System 327 13.3 Proposed System 327 13.4 Objectives of the Concept 328 13.5 Operational Requirements 328 13.6 Non-Operational Requirements 329 13.7 Depiction Design Description 330 13.8 System Architecture 330 13.8.1 Module Characteristics 331 13.8.2 Convolutional Neural System 332 13.8.3 User Application 332 13.9 Design Diagrams 333 13.9.1 High-Level Design 333 13.9.2 Low-Level Design 333 13.9.3 Test Cases 335 13.10 Comparison and Screenshot 335 13.11 Conclusion 342 References 342 14 Prediction of Stock Market Using Machine Learning–Based Data Analytics 347 Maheswari P. and Jaya A. 14.1 Introduction of Stock Market 348 14.1.1 Impact of Stock Prices 349 14.2 Related Works 350 14.3 Financial Prediction Systems Framework 352 14.3.1 Conceptual Financial Prediction Systems 352 14.3.2 Framework of Financial Prediction Systems Using Machine Learning 353 14.3.2.1 Algorithm to Predicting the Closing Price of the Given Stock Data Using Linear Regression 355 14.3.3 Framework of Financial Prediction Systems Using Deep Learning 355 14.3.3.1 Algorithm to Predict the Closing Price of the Given Stock Using Long Short-Term Memory 356 14.4 Implementation and Discussion of Result 357 14.4.1 Pharmaceutical Sector 357 14.4.1.1 Cipla Limited 357 14.4.1.2 Torrent Pharmaceuticals Limited 359 14.4.2 Banking Sector 359 14.4.2.1 ICICI Bank 359 14.4.2.2 State Bank of India 359 14.4.3 Fast-Moving Consumer Goods Sector 362 14.4.3.1 ITC 363 14.4.3.2 Hindustan Unilever Limited 363 14.4.4 Power Sector 363 14.4.4.1 Adani Power Limited 363 14.4.4.2 Power Grid Corporation of India Limited 364 14.4.5 Automobiles Sector 368 14.4.5.1 Mahindra & Mahindra Limited 368 14.4.5.2 Maruti Suzuki India Limited 368 14.4.6 Comparison of Prediction Using Linear Regression Model and Long-Short-Term Memory Model 368 14.5 Conclusion 371 14.5.1 Future Enhancement 372 References 372 Web Citations 373 15 Pehchaan: Analysis of the ‘Aadhar Dataset’ to Facilitate a Smooth and Efficient Conduct of the Upcoming NPR 375 Soumyadev Mukherjee, Harshit Anand, Nishan Acharya, Subham Char, Pritam Ghosh and MinakhiRout 15.1 Introduction 376 15.2 Basic Concepts 377 15.3 Study of Literature Survey and Technology 380 15.4 Proposed Model 381 15.5 Implementation and Results 383 15.6 Conclusion 389 References 389 16 Deep Learning Approach for Resource Optimization in Blockchain, Cellular Networks, and IoT: Open Challenges and Current Solutions 391 Upinder Kaur and Shalu 16.1 Introduction 392 16.1.1 Aim 393 16.1.2 Research Contribution 395 16.1.3 Organization 396 16.2 Background 396 16.2.1 Blockchain 397 16.2.2 Internet of Things (IoT) 398 16.2.3 5G Future Generation Cellular Networks 398 16.2.4 Machine Learning and Deep Learning Techniques 399 16.2.5 Deep Reinforcement Learning 399 16.3 Deep Learning for Resource Management in Blockchain, Cellular, and IoT Networks 401 16.3.1 Resource Management in Blockchain for 5G Cellular Networks 402 16.3.2 Deep Learning Blockchain Application for Resource Management in IoT Networks 402 16.4 Future Research Challenges 413 16.4.1 Blockchain Technology 413 16.4.1.1 Scalability 414 16.4.1.2 Efficient Consensus Protocols 415 16.4.1.3 Lack of Skills and Experts 415 16.4.2 IoT Networks 416 16.4.2.1 Heterogeneity of IoT and 5G Data 416 16.4.2.2 Scalability Issues 416 16.4.2.3 Security and Privacy Issues 416 16.4.3 5G Future Generation Networks 416 16.4.3.1 Heterogeneity 416 16.4.3.2 Security and Privacy 417 16.4.3.3 Resource Utilization 417 16.4.4 Machine Learning and Deep Learning 417 16.4.4.1 Interpretability 418 16.4.4.2 Training Cost for ML and DRL Techniques 418 16.4.4.3 Lack of Availability of Data Sets 418 16.4.4.4 Avalanche Effect for DRL Approach 419 16.4.5 General Issues 419 16.4.5.1 Security and Privacy Issues 419 16.4.5.2 Storage 419 16.4.5.3 Reliability 420 16.4.5.4 Multitasking Approach 420 16.5 Conclusion and Discussion 420 References 422 17 Unsupervised Learning in Accordance With New Aspects of Artificial Intelligence 429 Riya Sharma, Komal Saxena and Ajay Rana 17.1 Introduction 430 17.2 Applications of Machine Learning in Data Management Possibilities 431 17.2.1 Terminology of Basic Machine Learning 432 17.2.2 Rules Based on Machine Learning 434 17.2.3 Unsupervised vs. Supervised Methodology 434 17.3 Solutions to Improve Unsupervised Learning Using Machine Learning 436 17.3.1 Insufficiency of Labeled Data 436 17.3.2 Overfitting 437 17.3.3 A Closer Look Into Unsupervised Algorithms 437 17.3.3.1 Reducing Dimensionally 437 17.3.3.2 Principal Component Analysis 438 17.3.4 Singular Value Decomposition (SVD) 439 17.3.4.1 Random Projection 439 17.3.4.2 Isomax 439 17.3.5 Dictionary Learning 439 17.3.6 The Latent Dirichlet Allocation 440 17.4 Open Source Platform for Cutting Edge Unsupervised Machine Learning 440 17.4.1 TensorFlow 441 17.4.2 Keras 441 17.4.3 Scikit-Learn 441 17.4.4 Microsoft Cognitive Toolkit 442 17.4.5 Theano 442 17.4.6 Caffe 442 17.4.7 Torch 442 17.5 Applications of Unsupervised Learning 443 17.5.1 Regulation of Digital Data 443 17.5.2 Machine Learning in Voice Assistance 443 17.5.3 For Effective Marketing 444 17.5.4 Advancement of Cyber Security 444 17.5.5 Faster Computing Power 444 17.5.6 The Endnote 445 17.6 Applications Using Machine Learning Algos 445 17.6.1 Linear Regression 445 17.6.2 Logistic Regression 446 17.6.3 Decision Tree 446 17.6.4 Support Vector Machine (SVM) 446 17.6.5 Naive Bayes 446 17.6.6 K-Nearest Neighbors 447 17.6.7 K-Means 447 17.6.8 Random Forest 447 17.6.9 Dimensionality Reduction Algorithms 448 17.6.10 Gradient Boosting Algorithms 448 References 449 18 Predictive Modeling of Anthropomorphic Gamifying Blockchain-Enabled Transitional Healthcare System 461 Deepa Kumari, B.S.A.S. Rajita, Medindrao Raja Sekhar, Ritika Garg and Subhrakanta Panda 18.1 Introduction 462 18.1.1 Transitional Healthcare Services and Their Challenges 462 18.2 Gamification in Transitional Healthcare: A New Model 463 18.2.1 Anthropomorphic Interface With Gamification 464 18.2.2 Gamification in Blockchain 465 18.2.3 Anthropomorphic Gamification in Blockchain: Motivational Factors 466 18.3 Existing Related Work 468 18.4 The Framework 478 18.4.1 Health Player 479 18.4.2 Data Collection 480 18.4.3 Anthropomorphic Gamification Layers 480 18.4.4 Ethereum 480 18.4.4.1 Ethereum-Based Smart Contracts for Healthcare 481 18.4.4.2 Installation of Ethereum Smart Contract 481 18.4.5 Reward Model 482 18.4.6 Predictive Models 482 18.5 Implementation 483 18.5.1 Methodology 483 18.5.2 Result Analysis 484 18.5.3 Threats to the Validity 486 18.6 Conclusion 487 References 487 Index 491

Autorenportrait

Audience Researchers and industry engineers in computer science and artificial intelligence, IT professionals, network administrators, cybersecurity experts. Sachi Nandan Mohanty received his PhD from IIT Kharagpur 2015 and he is now an associate professor in the Department of Computer Science & Engineering at ICFAI Foundation for Higher Education, Hyderabad, India. Jyotir Moy Chatterjee is an assistant professor in the IT Department at Lord Buddha Education Foundation (Asia Pacific University of Technology & Innovation), Kathmandu, Nepal. Monika Mangla received her PhD from Thapar Institute of Engineering & Technology, Patiala, Punjab in 2019, and is now an assistant professor in the Department of Computer Engineering at Lokmanya Tilak College of Engineering (LTCoE), Navi Mumbai, India. Suneeta Satpathy received her PhD from Utkal University, Bhubaneswar, Odisha in 2015, and is now an associate professor in the Department of Computer Science & Engineering at College of Engineering Bhubaneswar (CoEB), Bhubaneswar, India. Ms. Sirisha Potluri is an assistant professor in the Department of Computer Science & Engineering at ICFAI Foundation for Higher Education, Hyderabad, India.

Back cover copy

The book covers the multidimensional perspective of machine learning through the perspective of cloud computing and Internet of Things ranging from fundamentals to advanced applications Sustainable computing paradigms like cloud and fog are capable of handling issues related to performance, storage and processing, maintenance, security, efficiency, integration, cost, energy and latency in an expeditious manner. In order to expedite decision-making involved in the complex computation and processing of collected data, IoT devices are connected to the cloud or fog environment. Since machine learning as a service provides the best support in business intelligence, organizations have been making significant investments in this technology. Machine Learning Approach for Cloud Data Analytics in IoT elucidates some of the best practices and their respective outcomes in cloud and fog computing environments. It focuses on all the various research issues related to big data storage and analysis, large-scale data processing, knowledge discovery and knowledge management, computational intelligence, data security and privacy, data representation and visualization, and data analytics. The featured technologies presented in the book optimizes various industry processes using business intelligence in engineering and technology. Light is also shed on cloud-based embedded software development practices to integrate complex machines so as to increase productivity and reduce operational costs. The various practices of data science and analytics which are used in all sectors to understand big data and analyze massive data patterns are also detailed in the book.