Data Science with Semantic Technologies: Theory, Practice and Application

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