دانلود کتاب Data Science with Semantic Technologies: Theory, Practice and Application

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Cover Half-Title Page Title Page Copyright Page Contents Preface 1 A Brief Introduction and Importance of Data Science 1.1 What is Data Science? What Does a Data Scientist Do? 1.2 Why Data Science is in Demand? 1.3 History of Data Science 1.4 How Does Data Science Differ from Business Intelligence? 1.5 Data Science Life Cycle 1.6 Data Science Components 1.7 Why Data Science is Important 1.8 Current Challenges 1.8.1 Coordination, Collaboration, and Communication 1.8.2 Building Data Analytics Teams 1.8.3 Stakeholders vs Analytics 1.8.4 Driving with Data 1.9 Tools Used for Data Science 1.10 Benefits and Applications of Data Science Benefits of Data Science Applications of Data Science 1.11 Conclusion References 2 Exploration of Tools for Data Science 2.1 Introduction 2.2 Top Ten Tools for Data Science 2.3 Python for Data Science 2.3.1 Python Datatypes 2.3.2 Helpful Rules for Python Programming 2.3.3 Jupyter Notebook for IPython 2.3.4 Your First Python Program 2.4 R Language for Data Science 2.4.1 R Datatypes 2.4.2 Your First R Program 2.5 SQL for Data Science 2.6 Microsoft Excel for Data Science 2.6.1 Detection of Outliers in Data Sets Using Microsoft Excel 2.6.2 Regression Analysis in Excel Using Microsoft Excel 2.7 D3.JS for Data Science 2.8 Other Important Tools for Data Science 2.8.1 Apache Spark Ecosystem 2.8.2 MongoDB Data Store System 2.8.3 MATLAB Computing System 2.8.4 Neo4j for Graphical Database 2.8.5 VMWare Platform for Virtualization 2.9 Conclusion References 3 Data Modeling as Emerging Problems of Data Science 3.1 Introduction 3.2 Data 3.2.1 Unstructured Data 3.2.2 Semistructured Data 3.2.3 Structured Data 3.2.4 Hybrid (Un/Semi)-Structured Data 3.2.5 Big Data 3.3 Data Model Design 3.4 Data Modeling 3.4.1 Records-Based Data Model 3.4.2 Non–Record-Based Data Model 3.5 Polyglot Persistence Environment References 4 Data Management as Emerging Problems of Data Science 4.1 Introduction 4.2 Perspective and Context 4.2.1 Life Cycle 4.2.2 Use 4.3 Data Distribution 4.4 CAP Theorem 4.5 Polyglot Persistence References 5 Role of Data Science in Healthcare 5.1 Predictive Modeling—Disease Diagnosis and Prognosis 5.1.1 Supervised Machine Learning Models 5.1.2 Clustering Models 5.1.2.1 Centroid-Based Clustering Models 5.1.2.2 Expectation Maximization (EM) Algorithm 5.1.2.3 DBSCAN 5.1.3 Feature Engineering 5.2 Preventive Medicine—Genetics/Molecular Sequencing 5.2.1 Technologies for Sequencing 5.2.2 Sequence Data Analysis with BioPython 5.2.2.1 Sequence Data Formats 5.2.2.2 BioPython 5.3 Personalized Medicine 5.4 Signature Biomarkers Discovery from High Throughput Data 5.4.1 Methodology I — Novel Feature Selection Method with Improved Mutual Information and Fisher Score 5.4.1.1 Algorithm for the Novel Feature Selection Method with Improved Mutual Information and Fisher Score 5.4.1.2 Computing F-Score Values for the Features 5.4.1.3 Block Diagram for the Method-1 5.4.2 Feature Selection Methodology-II — Entropy Based Mean Score with mRMR 5.4.2.1 Algorithm for the Feature Selection Methodology-II 5.4.2.2 Introduction to mRMR Feature Selection 5.4.2.3 Data Sets 5.4.2.4 Identification of Biomarkers Using Rank Product 5.4.2.5 Fold Change Values Conclusion References 6 Partitioned Binary Search Trees (P(h)-BST): A Data Structure for Computer RAM 6.1 Introduction 6.2 P(h)-BST Structure 6.2.1 Preliminary Analysis 6.2.2 Terminology and Conventions 6.3 Maintenance Operations 6.3.1 Operations Inside a Class 6.3.2 Operations Between Classes (Outside a Class) 6.4 Insert and Delete Algorithms 6.4.1 Inserting a New Element 6.4.2 Deleting an Existing Element 6.5 P(h)-BST as a Generator of Balanced Binary Search Trees 6.6 Simulation Results 6.6.1 Data Structures and Abstract Data Types 6.6.2 Analyzing the Insert and Delete Process in Random Case 6.6.3 Analyzing the Insert Process in Ascending (Descending) Case 6.6.4 Comparing P(2)-BST/P(8)-BST to Red-Black/AVL Trees 6.7 Conclusion Acknowledgments References 7 Security Ontologies: An Investigation of Pitfall Rate 7.1 Introduction 7.2 Secure Data Management in the Semantic Web 7.3 Security Ontologies in a Nutshell 7.4 InFra_OE Framework 7.5 Conclusion References 8 IoT-Based Fully-Automated Fire Control System 8.1 Introduction 8.2 Related Works 8.3 Proposed Architecture 8.4 Major Components 8.4.1 Arduino UNO 8.4.2 Temperature Sensor 8.4.3 LCD Display (16X2) 8.4.4 Temperature Humidity Sensor (DHT11) 8.4.5 Moisture Sensor 8.4.6 CO2 Sensor 8.4.7 Nitric Oxide Sensor 8.4.8 CO Sensor (MQ-9) 8.4.9 Global Positioning System (GPS) 8.4.10 GSM Modem 8.4.11 Photovoltaic System 8.5 Hardware Interfacing 8.6 Software Implementation LM35 interfacing and loop programming DHT11 interfacing and loop programming MQ-X Interfacing and Loop Programming Moisture Sensor Interfacing and Loop Programming 8.7 Conclusion References 9 Phrase Level-Based Sentiment Analysis Using Paired Inverted Index and Fuzzy Rule 9.1 Introduction 9.2 Literature Survey 9.3 Methodology 9.3.1 Construction of Inverted Wordpair Index 9.3.1.1 Sentiment Analysis Design Framework 9.3.1.2 Sentiment Classification 9.3.1.3 Preprocessing of Data 9.3.1.4 Algorithm to Find the Score 9.3.1.5 Fuzzy System 9.3.1.6 Lexicon-Based Sentiment Analysis 9.3.1.7 Defuzzification 9.3.2 Performance Metrics 9.4 Conclusion References 10 Semantic Technology Pillars: The Story So Far 10.1 The Road that Brought Us Here 10.2 What is a Semantic Pillar? 10.2.1 Machine Learning 10.2.2 The Semantic Approach 10.3 The Foundation Semantic Pillars: IRI’s, RDF, and RDFS 10.3.1 Internationalized Resource Identifier (IRI) 10.3.2 Resource Description Framework (RDF) 10.3.2.1 Alternative Technologies to RDF: Property Graphs 10.3.3 RDF Schema (RDFS) 10.4 The Semantic Upper Pillars: OWL, SWRL, SPARQL, and SHACL 10.4.1 The Web Ontology Language (OWL) 10.4.1.1 Axioms to Define Classes 10.4.1.2 The Open World Assumption 10.4.1.3 No Unique Names Assumption 10.4.1.4 Serialization 10.4.2 The Semantic Web Rule Language 10.4.2.1 The Limitations of Monotonic Reasoning 10.4.2.2 Alternatives to SWRL 10.4.3 SPARQL 10.4.3.1 The SERVICE Keyword and Linked Data 10.4.4 SHACL 10.4.4.1 The Fundamentals of SHACL 10.5 Conclusion References 11 Evaluating Richness of Security Ontologies for Semantic Web 11.1 Introduction 11.2 Ontology Evaluation: State-of-the-Art 11.2.1 Domain-Dependent Ontology Evaluation Tools 11.2.2 Domain-Independent Ontology Evaluation Tools 11.3 Security Ontology 11.4 Richness of Security Ontologies 11.5 Conclusion References 12 Health Data Science and Semantic Technologies 12.1 Health Data 12.2 Data Science 12.3 Health Data Science 12.4 Examples of Health Data Science Applications 12.5 Health Data Science Challenges 12.6 Health Data Science and Semantic Technologies 12.6.1 Natural Language Processing (NLP) 12.6.2 Clinical Data Sharing and Data Integration 12.6.3 Ontology Engineering and Quality Assurance (QA) 12.7 Application of Data Science for COVID-19 12.8 Data Challenges During COVID-19 Outbreak 12.9 Biomedical Data Science 12.10 Conclusion References 13 Hybrid Mixed Integer Optimization Method for Document Clustering Based on Semantic Data Matrix 13.1 Introduction 13.2 A Method for Constructing a Semantic Matrix of Relations Between Documents and Taxonomy Concepts 13.3 Mathematical Statements for Clustering Problem 13.3.1 Mathematical Statements for PDC Clustering Problem 13.3.2 Mathematical Statements for CC Clustering Problem 13.3.3 Relations between PDC Clustering and CC Clustering 13.4 Heuristic Hybrid Clustering Algorithm 13.5 Application of a Hybrid Optimization Algorithm for Document Clustering 13.6 Conclusion Acknowledgment References 14 Role of Knowledge Data Science During COVID-19 Pandemic 14.1 Introduction 14.1.1 Global Health Emergency 14.1.2 Timeline of the COVID-19 14.2 Literature Review 14.3 Model Discussion 14.3.1 COVID-19 Time Series Dataset 14.3.2 FBProphet Forecasting Model 14.3.3 Data Preprocessing 14.3.4 Data Visualization 14.4 Results and Discussions 14.4.1 Analysis and Forecasting: The World 14.4.2 Performance Metrics 14.4.3 Analysis and Forecasting: The Top 20 Countries 14.5 Conclusion References 15 Semantic Data Science in the COVID-19 Pandemic 15.1 Crises Often Are Catalysts for New Technologies 15.1.1 Definitions 15.1.2 Methodology 15.2 The Domains of COVID-19 Semantic Data Science Research 15.2.1 Surveys 15.2.2 Semantic Search 15.2.2.1 Enhancing the CORD-19 Dataset with Semantic Data 15.2.2.2 CORD-19-on-FHIR -Semantics for COVID-19 Discovery 15.2.2.3 Semantic Search on Amazon Web Services (AWS) 15.2.2.4 COVID*GRAPH 15.2.2.5 Network Graph Visualization of CORD-19 15.2.2.6 COVID-19 on the Web 15.2.3 Statistics 15.2.3.1 The Johns Hopkins COVID-19 Dashboard 15.2.3.2 The NY Times Dataset 15.2.4 Surveillance 15.2.4.1 An IoT Framework for Remote Patient Monitoring 15.2.4.2 Risk Factor Discovery 15.2.4.3 COVID-19 Surveillance in a Primary Care Network 15.2.5 Clinical Trials 15.2.6 Drug Repurposing 15.2.7 Vocabularies 15.2.8 Data Analysis 15.2.8.1 CODO 15.2.8.2 COVID-19 Phenotypes 15.2.8.3 Detection of “Fake News” 15.2.8.4 Ontology-Driven Weak Supervision for Clinical Entity Classification 15.2.9 Harmonization 15.3 Discussion 15.3.1 Privacy Issues 15.3.2 Domains that May Currently be Under Utilized 15.3.2.1 Detection of Fake News 15.3.2.2 Harmonization 15.3.3 Machine Learning and Semantic Technology: Synergy Not Competition 15.3.4 Conclusion Acknowledgment References Index EULA