Data Engineering and Data Science : Concepts and Applications

Data Engineering and Data Science Concepts and Applications

This book, 'Advances in Data Engineering and Machine Learning Engineering', explores the practical applications of data collection, analysis, and management. It focuses on the roles of data engineers, data scientists, and machine learning engineers in enhancing business processes through d...

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Detalles Bibliográficos
Autor principal: Kumar, Kukatlapalli Pradeep (-)
Otros Autores: Unal, Aynur, Pillai, Vinay Jha, Murthy, Hari, Niranjanamurthy, M.
Formato: Libro electrónico
Idioma:Inglés
Publicado: Newark : John Wiley & Sons, Incorporated 2023.
Edición:1st ed
Materias:
Machine learning.
Data mining.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009811327206719
Tabla de Contenidos:
  • Cover
  • Title Page
  • Copyright Page
  • Contents
  • Preface
  • Chapter 1 Quality Assurance in Data Science: Need, Challenges and Focus
  • 1.1 Introduction
  • 1.1.1 Quality Assurance and Testing
  • 1.1.2 Data Science and Quality Assurance
  • 1.1.3 Background
  • 1.2 Testing and Quality Assurance
  • 1.2.1 Key Terminologies Associated With Testing
  • 1.3 Product Quality and Test Efforts
  • 1.3.1 Testing Metrics
  • 1.3.2 How to Improve the Business Value to Products Using Test Automation
  • 1.3.3 Data Analysis and Management in Test Automation
  • 1.3.4 Data Models in Data Science
  • 1.4 Data Masking in Data Model and Associated Risks
  • 1.5 Prediction in Data Science
  • Case Study
  • 1.6 Role of Metrics in Evaluation
  • 1.7 Quantity of Data in Quality Assurance
  • 1.8 Identifying the Right Data Sources
  • 1.8.1 Need to Gather Up-to-Date Data
  • 1.8.2 Synthesising Existing Advanced Technologies for Continuous Business Improvements
  • 1.9 Conclusion
  • References
  • Chapter 2 Design and Implementation of Social Media Mining - Knowledge Discovery Methods for Effective Digital Marketing Strategies
  • 2.1 Introduction
  • 2.1.1 Objectives of the Study
  • 2.2 Literature Review
  • 2.3 Novel Framework for Social Media Data Mining and Knowledge Discovery
  • 2.4 Classification for Comparison Analysis
  • 2.5 Clustering Methodology to Provide Digital Marketing Strategies
  • 2.5.1 Status (Text Form)
  • 2.5.2 Images (Photos)
  • 2.5.3 Video Post
  • 2.5.4 Link Post
  • 2.6 Experimental Results
  • 2.7 Conclusion
  • References
  • Chapter 3 A Study on Big Data Engineering Using Cloud Data Warehouse
  • 3.1 Introduction
  • 3.2 Comparison Study of Different Cloud Data Warehouses
  • 3.2.1 Amazon Redshift
  • 3.2.2 High-Level Architecture of Amazon Redshift
  • 3.2.3 Features of Amazon Redshift Cloud Data Warehouse
  • 3.2.4 Pricing of Amazon Redshift Cloud Data Warehouse.
  • 3.3 Snowflake Cloud Data Warehouse
  • 3.3.1 High-Level Architecture of Snowflake Cloud Data Warehouse
  • 3.3.2 Features of Snowflake Cloud Data Warehouse
  • 3.3.3 Snowflake Cloud Data Warehouse Pricing
  • 3.4 Google BigQuery Cloud Data Warehouse
  • 3.4.1 High-Level Architecture of Google BigQuery Cloud Data Warehouse
  • 3.4.2 Features of Google BigQuery Cloud Data Warehouse
  • 3.4.3 Google BigQuery Cloud Data Warehouse Pricing
  • 3.5 Microsoft Azure Synapse Cloud Data Warehouse
  • 3.5.1 Microsoft Azure Synapse Cloud Data Warehouse Architecture
  • 3.5.2 Features of Microsoft Azure Synapse Cloud Data Warehouse
  • 3.5.3 Pricing of Microsoft Azure Synapse Cloud Data Warehouse
  • 3.6 Informatica Intelligent Cloud Services (IICS)
  • 3.6.1 Informatica Intelligent Cloud Services Architecture
  • 3.6.2 Salient Features of Informatica Intelligent Cloud Services
  • 3.6.3 Informatica Intelligent Cloud Services Pricing Model
  • 3.7 Conclusion
  • Acknowledgements
  • References
  • Chapter 4 Data Mining with Cluster Analysis Through Partitioning Approach of Huge Transaction Data
  • 4.1 Introduction
  • 4.2 Methodology Used in Proposed Cluster Analysis System
  • 4.2.1 Design of Algorithms
  • 4.3 Literature Survey on Existing Systems
  • 4.3.1 Experimental Results
  • 4.4 Conclusion
  • References
  • Chapter 5 Application of Data Science in Macromodeling of Nonlinear Dynamical Systems
  • 5.1 Introduction
  • 5.2 Nonlinear Autonomous Dynamical System
  • 5.3 Nonlinear System - MOR
  • 5.3.1 Proper Orthogonal Decomposition
  • 5.4 Data Science Life Cycle
  • 5.4.1 Problem Identification
  • 5.4.2 Identifying Available Data Sources and Data Collection
  • 5.4.3 Data Processing
  • 5.4.4 Data Exploration
  • 5.4.5 Feature Extraction
  • 5.4.6 Modeling
  • 5.4.7 Model Performance Evaluation
  • 5.5 Artificial Neural Network in Modeling
  • 5.5.1 Machine Learning.
  • 5.5.2 Biological Neuron Model
  • 5.5.3 Artificial Neural Networks
  • 5.5.4 Network Topologies
  • 5.5.4.1 NARX Neural Network
  • 5.5.5 ANN Modeling Using Mathematical Models
  • 5.6 Neuron Spiking Model Using FitzHugh-Nagumo (F-N) System
  • 5.6.1 Linearization of F-N System
  • 5.6.2 Reduced Order Model of Linear System
  • 5.6.3 Finite Difference Discretization of F-N System
  • 5.6.4 MOR of F-N System Using POD-Galerkin Method
  • 5.7 Ring Oscillator Model
  • 5.7.1 Model Order Reduction of Ring Oscillator Circuit
  • 5.7.2 Ring Oscillator Circuit Approximation Using Linear System MOR
  • 5.7.3 POD-ANN Macromodel of Ring Oscillator Circuit
  • 5.8 Nonlinear VLSI Interconnect Model Using Telegraph Equation
  • 5.8.1 Macromodeling of VLSI Interconnect
  • 5.8.2 Discretisation of Interconnect Model
  • 5.8.3 Linearization of VLSI Interconnect Model
  • 5.8.4 Reduced Order Linear Model of VLSI Interconnect
  • 5.9 Macromodel Using Machine Learning
  • 5.9.1 Activation Function
  • 5.9.2 Bayesian Regularization
  • 5.9.3 Optimization
  • 5.10 MOR of Dynamical Systems Using POD-ANN
  • 5.10.1 Accuracy and Performance Index
  • 5.11 Numerical Results
  • 5.11.1 F-N System
  • 5.11.2 Ring Oscillator Model
  • 5.11.3 Reduced Order POD Approximation of Ring Oscillator
  • 5.11.3.1 Study of POD-ANN Approximation of Ring Oscillator for Variation in Amplitude of Input Signal and for Different Input Signals
  • 5.11.3.2 POD-ANN Approximation of Ring Oscillator for Variation in Frequency
  • 5.11.4 POD-ANN Approximation of VLSI Interconnect
  • 5.12 Conclusion
  • References
  • Chapter 6 Comparative Analysis of Various Ensemble Approaches for Web Page Classification
  • 6.1 Introduction
  • 6.2 Literature Survey
  • 6.3 Material and Methods
  • 6.4 Ensemble Classifiers
  • 6.4.1 Bagging
  • 6.4.1.1 Bagging Meta Estimator
  • 6.4.1.2 Random Forest
  • 6.4.2 Boosting
  • 6.4.2.1 AdaBoost.
  • 6.4.2.2 Gradient Tree Boosting
  • 6.4.2.3 XGBoost
  • 6.4.3 Stacking
  • 6.5 Results
  • 6.5.1 Bagging Meta Estimator
  • 6.5.2 Random Forest
  • 6.5.3 AdaBoost
  • 6.5.4 Gradient Tree Boosting
  • 6.5.5 XGBoost
  • 6.5.6 Stacking
  • 6.5.7 Comparison with Single Classifiers
  • 6.6 Conclusion
  • Acknowledgement
  • References
  • Chapter 7 Feature Engineering and Selection Approach Over Malicious Image
  • 7.1 Introduction
  • 7.2 Feature Engineering Techniques
  • 7.2.1 Methodologies in Feature Engineering
  • 7.2.2 Strides in Feature Engineering
  • 7.2.3 Feature Extraction
  • 7.2.4 Feature Selection
  • 7.2.5 Feature Engineering in Image Processing
  • 7.2.6 Importance of Feature Engineering in Image Processing
  • 7.3 Malicious Feature Engineering
  • 7.4 Image Processing Technique
  • 7.4.1 Steps Involved in Image Processing Technique
  • 7.4.2 Image Processing Task
  • 7.4.2.1 Image Enhancement
  • 7.4.2.2 Image Restoration
  • 7.4.2.3 Coloring Image Processing
  • 7.4.2.4 Wavelets Processing and Multiple Solutions
  • 7.4.2.5 Image Compression
  • 7.4.2.6 Character Recognition
  • 7.4.2.7 Characteristics of Image Processing
  • 7.5 Image Processing Techniques for Analysis on Malicious Images
  • 7.6 Conclusion
  • References
  • Blog
  • Chapter 8 Cubic-Regression and Likelihood Based Boosting GAM to Model Drug Sensitivity for Glioblastoma
  • 8.1 Introduction
  • 8.1.1 Glioblastoma
  • 8.2 Literature Survey
  • 8.3 Materials and Methods
  • 8.3.1 Methodology
  • 8.3.1.1 Generalized Additive Models (GAMs)
  • 8.3.1.2 Model-Based Boosting - Boosted GAM
  • 8.3.2 Datasets Description
  • 8.4 Evaluations, Results and Discussions
  • 8.4.1 Akaike Information Criterion (AIC)
  • 8.4.2 Adjusted R-Squared
  • 8.4.3 Discussion
  • Conclusion
  • References
  • Chapter 9 Unobtrusive Engagement Detection through Semantic Pose Estimation and Lightweight ResNet for an Online Class Environment.
  • 9.1 Introduction
  • 9.2 Related Work
  • 9.2.1 Analysis for a Classroom Environment
  • 9.2.2 Pose Estimation
  • 9.2.3 Face Alignment and Landmark Estimation
  • 9.2.4 Deep Networks for Emotional Analysis
  • 9.3 Proposed Methodology
  • 9.3.1 Data Description
  • 9.3.2 Facial Detection and Recognition
  • 9.3.2.1 Face Detection
  • 9.3.2.2 Facial Landmark Detection
  • 9.3.3 Emotion Quantification
  • 9.3.4 Pose Estimation
  • 9.3.4.1 Facial Pose Estimation
  • 9.4 Experimentation
  • 9.5 Results and Discussions
  • Conclusion
  • References
  • Chapter 10 Building Rule Base for Decision Making - A Fuzzy-Rough Approach
  • 10.1 Introduction
  • 10.2 Literature Review
  • 10.3 Discretization of the Dataset Using Fuzzy Set Theory
  • 10.4 Description of the Dataset
  • 10.5 Process Involved in Proposed Work
  • 10.6 Experiment
  • 10.7 Evaluation Result
  • 10.8 Discussion
  • Conclusion
  • References
  • Chapter 11 An Effective Machine Learning Approach to Model Healthcare Data
  • 11.1 Introduction
  • 11.2 Types of Data in Healthcare
  • 11.3 Big Data in Healthcare
  • 11.4 Different V's of Big Data
  • 11.5 About COPD
  • 11.6 Methodology Implemented
  • Conclusion
  • References
  • Chapter 12 Recommendation Engine for Retail Domain Using Machine Learning Techniques
  • 12.1 Introduction
  • 12.2 Proposed System
  • 12.2.1 Classification of Suppliers
  • 12.2.2 Recommendation for Buyer
  • 12.2.3 Forecasting Using ARIMA Model
  • 12.3 Results
  • 12.3.1 ARIMA Forecasting
  • 12.4 Conclusion
  • References
  • Chapter 13 Mining Heterogeneous Lung Cancer from Computer Tomography (CT) Scan with the Confusion Matrix
  • 13.1 Introduction
  • 13.2 Literature Review
  • 13.3 Methodology
  • 13.3.1 Description of the Data
  • 13.3.2 Image Preprocessing
  • 13.3.3 Image Segmentation
  • 13.3.4 Image Processing
  • 13.3.5 Zero Component Analysis (ZCA) Whitening
  • 13.3.6 Local Binary Pattern (LBP Feature).
  • 13.3.7 LESH Vector.

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