Data Science with Semantic Technologies
Wiley-Scrivener (Verlag)
978-1-119-86498-1 (ISBN)
- Lieferbar
- Versandkostenfrei
- Auch auf Rechnung
- Artikel merken
To create intelligence in data science, it becomes necessary to utilize semantic technologies which allow machine-readable representation of data. This intelligence uniquely identifies and connects data with common business terms, and it also enables users to communicate with data. Instead of structuring the data, semantic technologies help users to understand the meaning of the data by using the concepts of semantics, ontology, OWL, linked data, and knowledge-graphs. These technologies help organizations to understand all the stored data, adding the value in it, and enabling insights that were not available before. As data is the most important asset for any organization, it is essential to apply semantic technologies in data science to fulfill the need of any organization.
Data Science with Semantic Technologies provides a roadmap for the deployment of semantic technologies in the field of data science. Moreover, it highlights how data science enables the user to create intelligence through these technologies by exploring the opportunities and eradicating the challenges in the current and future time frame. In addition, this book provides answers to various questions like: Can semantic technologies be able to facilitate data science? Which type of data science problems can be tackled by semantic technologies? How can data scientists benefit from these technologies? What is knowledge data science? How does knowledge data science relate to other domains? What is the role of semantic technologies in data science? What is the current progress and future of data science with semantic technologies? Which types of problems require the immediate attention of researchers?
Audience
Researchers in the fields of data science, semantic technologies, artificial intelligence, big data, and other related domains, as well as industry professionals, software engineers/scientists, and project managers who are developing the software for data science. Students across the globe will get the basic and advanced knowledge on the current state and potential future of data science.
Archana Patel, PhD, is a faculty of the Department of Software Engineering, School of Computing and Information Technology, Binh Duong Province, Vietnam. She completed her Postdoc from the Freie Universität Berlin, Berlin, Germany. Dr. Patel is an author or co-author of more than 30 publications in numerous refereed journals and conference proceedings. She has been awarded the Best Paper award (three times) at international conferences. Her research interests are ontological engineering, semantic web, big data, expert systems, and knowledge warehouse. Narayan C. Debnath, PhD, is the Founding Dean of the School of Computing and Information Technology at Eastern International University, Vietnam. He is also serving as the Head of the Department of Software Engineering at Eastern International University, Vietnam. Dr. Debnath has been the Director of the International Society for Computers and their Applications (ISCA), USA since 2014. Formerly, Dr. Debnath served as a Full Professor of Computer Science at Winona State University, Minnesota, USA for 28 years. Bharat Bhusan, PhD, is an assistant professor in the Department of Computer Science and Engineering, School of Engineering and Technology, Sharda University, India. In the last three years, he has published more than 80 research papers in various renowned international conferences and SCI indexed journals and edited 11 books.
Preface xv
1 A Brief Introduction and Importance of Data Science 1
Karthika N., Sheela J. and Janet B.
1.1 What is Data Science? What Does a Data Scientist Do? 2
1.2 Why Data Science is in Demand? 2
1.3 History of Data Science 4
1.4 How Does Data Science Differ from Business Intelligence? 9
1.5 Data Science Life Cycle 11
1.6 Data Science Components 13
1.7 Why Data Science is Important 14
1.8 Current Challenges 15
1.8.1 Coordination, Collaboration, and Communication 16
1.8.2 Building Data Analytics Teams 16
1.8.3 Stakeholders vs Analytics 17
1.8.4 Driving with Data 17
1.9 Tools Used for Data Science 19
1.10 Benefits and Applications of Data Science 28
1.11 Conclusion 28
References 29
2 Exploration of Tools for Data Science 31
Qasem Abu Al-Haija
2.1 Introduction 32
2.2 Top Ten Tools for Data Science 35
2.3 Python for Data Science 35
2.3.1 Python Datatypes 36
2.3.2 Helpful Rules for Python Programming 37
2.3.3 Jupyter Notebook for IPython 37
2.3.4 Your First Python Program 38
2.4 R Language for Data Science 39
2.4.1 R Datatypes 39
2.4.2 Your First R Program 41
2.5 SQL for Data Science 44
2.6 Microsoft Excel for Data Science 48
2.6.1 Detection of Outliers in Data Sets Using Microsoft Excel 48
2.6.2 Regression Analysis in Excel Using Microsoft Excel 50
2.7 D3.JS for Data Science 57
2.8 Other Important Tools for Data Science 58
2.8.1 Apache Spark Ecosystem 58
2.8.2 MongoDB Data Store System 60
2.8.3 MATLAB Computing System 62
2.8.4 Neo4j for Graphical Database 63
2.8.5 VMWare Platform for Virtualization 65
2.9 Conclusion 66
References 68
3 Data Modeling as Emerging Problems of Data Science 71
Mahyuddin K. M. Nasution and Marischa Elveny
3.1 Introduction 72
3.2 Data 72
3.2.1 Unstructured Data 74
3.2.2 Semistructured Data 74
3.2.3 Structured Data 76
3.2.4 Hybrid (Un/Semi)-Structured Data 77
3.2.5 Big Data 78
3.3 Data Model Design 79
3.4 Data Modeling 81
3.4.1 Records-Based Data Model 81
3.4.2 Non–Record-Based Data Model 84
3.5 Polyglot Persistence Environment 87
References 88
4 Data Management as Emerging Problems of Data Science 91
Mahyuddin K. M. Nasution and Rahmad Syah
4.1 Introduction 92
4.2 Perspective and Context 92
4.2.1 Life Cycle 93
4.2.2 Use 95
4.3 Data Distribution 98
4.4 CAP Theorem 100
4.5 Polyglot Persistence 101
References 102
5 Role of Data Science in Healthcare 105
Anidha Arulanandham, A. Suresh and Senthil Kumar R.
5.1 Predictive Modeling—Disease Diagnosis and Prognosis 106
5.1.1 Supervised Machine Learning Models 107
5.1.2 Clustering Models 110
5.1.2.1 Centroid-Based Clustering Models 110
5.1.2.2 Expectation Maximization (EM) Algorithm 110
5.1.2.3 DBSCAN 111
5.1.3 Feature Engineering 111
5.2 Preventive Medicine—Genetics/Molecular Sequencing 111
5.2.1 Technologies for Sequencing 113
5.2.2 Sequence Data Analysis with BioPython 114
5.2.2.1 Sequence Data Formats 114
5.2.2.2 BioPython 117
5.3 Personalized Medicine 121
5.4 Signature Biomarkers Discovery from High Throughput Data 122
5.4.1 Methodology I — Novel Feature Selection Method with Improved Mutual Information and Fisher Score 123
5.4.1.1 Algorithm for the Novel Feature Selection Method with Improved Mutual Information and Fisher Score 124
5.4.1.2 Computing F-Score Values for the Features 125
5.4.1.3 Block Diagram for the Method-1 125
5.4.1.4 Data Set 126
5.4.1.5 Identification of Biomarkers Using the Feature Selection Technique-I 127
5.4.2 Feature Selection Methodology-II — Entropy Based Mean Score with mRMR 128
5.4.2.1 Algorithm for the Feature Selection Methodology-II 130
5.4.2.2 Introduction to mRMR Feature Selection 132
5.4.2.3 Data Sets 132
5.4.2.4 Identification of Biomarkers Using Rank Product 133
5.4.2.5 Fold Change Values 133
Conclusion 136
References 136
6 Partitioned Binary Search Trees (P(h)-BST): A Data Structure for Computer RAM 139
Pr. D.E Zegour
6.1 Introduction 140
6.2 P(h)-BST Structure 141
6.2.1 Preliminary Analysis 143
6.2.2 Terminology and Conventions 143
6.3 Maintenance Operations 143
6.3.1 Operations Inside a Class 145
6.3.2 Operations Between Classes (Outside a Class) 148
6.4 Insert and Delete Algorithms 153
6.4.1 Inserting a New Element 153
6.4.2 Deleting an Existing Element 157
6.5 P(h)-BST as a Generator of Balanced Binary Search Trees 160
6.6 Simulation Results 162
6.6.1 Data Structures and Abstract Data Types 164
6.6.2 Analyzing the Insert and Delete Process in Random Case 164
6.6.3 Analyzing the Insert Process in Ascending (Descending) Case 168
6.6.4 Comparing P(2)-BST/P(∞)-BST to Red-Black/AVL Trees 174
6.7 Conclusion 175
Acknowledgments 176
References 176
7 Security Ontologies: An Investigation of Pitfall Rate 179
Archana Patel and Narayan C. Debnath
7.1 Introduction 179
7.2 Secure Data Management in the Semantic Web 184
7.3 Security Ontologies in a Nutshell 187
7.4 InFra_OE Framework 189
7.5 Conclusion 193
References 193
8 IoT-Based Fully-Automated Fire Control System 199
Lalit Mohan Satapathy
8.1 Introduction 200
8.2 Related Works 201
8.3 Proposed Architecture 203
8.4 Major Components 205
8.4.1 Arduino UNO 205
8.4.2 Temperature Sensor 207
8.4.3 LCD Display (16X2) 208
8.4.4 Temperature Humidity Sensor (DHT11) 209
8.4.5 Moisture Sensor 210
8.4.6 CO2 Sensor 211
8.4.7 Nitric Oxide Sensor 212
8.4.8 CO Sensor (MQ-9) 212
8.4.9 Global Positioning System (GPS) 212
8.4.10 GSM Modem 213
8.4.11 Photovoltaic System 214
8.5 Hardware Interfacing 216
8.6 Software Implementation 218
8.7 Conclusion 222
References 223
9 Phrase Level-Based Sentiment Analysis Using Paired Inverted Index and Fuzzy Rule 225
Sheela J., Karthika N. and Janet B.
9.1 Introduction 226
9.2 Literature Survey 228
9.3 Methodology 233
9.3.1 Construction of Inverted Wordpair Index 234
9.3.1.1 Sentiment Analysis Design Framework 235
9.3.1.2 Sentiment Classification 236
9.3.1.3 Preprocessing of Data 237
9.3.1.4 Algorithm to Find the Score 240
9.3.1.5 Fuzzy System 240
9.3.1.6 Lexicon-Based Sentiment Analysis 241
9.3.1.7 Defuzzification 242
9.3.2 Performance Metrics 243
9.4 Conclusion 244
References 244
10 Semantic Technology Pillars: The Story So Far 247
Michael DeBellis, Jans Aasman and Archana Patel
10.1 The Road that Brought Us Here 248
10.2 What is a Semantic Pillar? 249
10.2.1 Machine Learning 249
10.2.2 The Semantic Approach 250
10.3 The Foundation Semantic Pillars: IRI’s, RDF, and RDFS 252
10.3.1 Internationalized Resource Identifier (IRI) 254
10.3.2 Resource Description Framework (RDF) 254
10.3.2.1 Alternative Technologies to RDF: Property Graphs 256
10.3.3 RDF Schema (RDFS) 257
10.4 The Semantic Upper Pillars: OWL, SWRL, SPARQL, and SHACL 259
10.4.1 The Web Ontology Language (OWL) 260
10.4.1.1 Axioms to Define Classes 262
10.4.1.2 The Open World Assumption 263
10.4.1.3 No Unique Names Assumption 263
10.4.1.4 Serialization 264
10.4.2 The Semantic Web Rule Language 264
10.4.2.1 The Limitations of Monotonic Reasoning 267
10.4.2.2 Alternatives to SWRL 267
10.4.3 SPARQL 268
10.4.3.1 The SERVICE Keyword and Linked Data 268
10.4.4 SHACL 271
10.4.4.1 The Fundamentals of SHACL 272
10.5 Conclusion 274
References 274
11 Evaluating Richness of Security Ontologies for Semantic Web 277
Ambrish Kumar Mishra, Narayan C. Debnath and Archana Patel
11.1 Introduction 277
11.2 Ontology Evaluation: State-of-the-Art 280
11.2.1 Domain-Dependent Ontology Evaluation Tools 281
11.2.2 Domain-Independent Ontology Evaluation Tools 282
11.3 Security Ontology 284
11.4 Richness of Security Ontologies 287
11.5 Conclusion 295
References 295
12 Health Data Science and Semantic Technologies 299
Haleh Ayatollahi
12.1 Health Data 300
12.2 Data Science 301
12.3 Health Data Science 301
12.4 Examples of Health Data Science Applications 304
12.5 Health Data Science Challenges 306
12.6 Health Data Science and Semantic Technologies 308
12.6.1 Natural Language Processing (NLP) 309
12.6.2 Clinical Data Sharing and Data Integration 310
12.6.3 Ontology Engineering and Quality Assurance (QA) 311
12.7 Application of Data Science for COVID-19 313
12.8 Data Challenges During COVID-19 Outbreak 314
12.9 Biomedical Data Science 315
12.10 Conclusion 316
References 317
13 Hybrid Mixed Integer Optimization Method for Document Clustering Based on Semantic Data Matrix 323
Tatiana Avdeenko and Yury Mezentsev
13.1 Introduction 324
13.2 A Method for Constructing a Semantic Matrix of Relations Between Documents and Taxonomy Concepts 327
13.3 Mathematical Statements for Clustering Problem 330
13.3.1 Mathematical Statements for PDC Clustering Problem 330
13.3.2 Mathematical Statements for CC Clustering Problem 334
13.3.3 Relations between PDC Clustering and CC Clustering 336
13.4 Heuristic Hybrid Clustering Algorithm 340
13.5 Application of a Hybrid Optimization Algorithm for Document Clustering 342
13.6 Conclusion 344
Acknowledgment 344
References 344
14 Role of Knowledge Data Science During COVID-19 Pandemic 347
Veena Kumari H. M. and D. S. Suresh
14.1 Introduction 348
14.1.1 Global Health Emergency 350
14.1.2 Timeline of the COVID-19 351
14.2 Literature Review 354
14.3 Model Discussion 356
14.3.1 COVID-19 Time Series Dataset 357
14.3.2 FBProphet Forecasting Model 358
14.3.3 Data Preprocessing 360
14.3.4 Data Visualization 360
14.4 Results and Discussions 362
14.4.1 Analysis and Forecasting: The World 362
14.4.2 Performance Metrics 371
14.4.3 Analysis and Forecasting: The Top 20 Countries 377
14.5 Conclusion 388
References 389
15 Semantic Data Science in the COVID-19 Pandemic 393
Michael DeBellis and Biswanath Dutta
15.1 Crises Often Are Catalysts for New Technologies 393
15.1.1 Definitions 394
15.1.2 Methodology 395
15.2 The Domains of COVID-19 Semantic Data Science Research 397
15.2.1 Surveys 398
15.2.2 Semantic Search 399
15.2.2.1 Enhancing the CORD-19 Dataset with Semantic Data 399
15.2.2.2 CORD-19-on-FHIR – Semantics for COVID-19 Discovery 400
15.2.2.3 Semantic Search on Amazon Web Services (AWS) 400
15.2.2.4 COVID*GRAPH 402
15.2.2.5 Network Graph Visualization of CORD-19 403
15.2.2.6 COVID-19 on the Web 404
15.2.3 Statistics 405
15.2.3.1 The Johns Hopkins COVID-19 Dashboard 405
15.2.3.2 The NY Times Dataset 406
15.2.4 Surveillance 406
15.2.4.1 An IoT Framework for Remote Patient Monitoring 406
15.2.4.2 Risk Factor Discovery 408
15.2.4.3 COVID-19 Surveillance in a Primary Care Network 408
15.2.5 Clinical Trials 409
15.2.6 Drug Repurposing 411
15.2.7 Vocabularies 414
15.2.8 Data Analysis 415
15.2.8.1 CODO 415
15.2.8.2 COVID-19 Phenotypes 416
15.2.8.3 Detection of “Fake News” 417
15.2.8.4 Ontology-Driven Weak Supervision for Clinical Entity Classification 417
15.2.9 Harmonization 418
15.3 Discussion 418
15.3.1 Privacy Issues 420
15.3.2 Domains that May Currently be Under Utilized 421
15.3.2.1 Detection of Fake News 421
15.3.2.2 Harmonization 421
15.3.3 Machine Learning and Semantic Technology: Synergy Not Competition 422
15.3.4 Conclusion 423
Acknowledgment 423
References 423
Index 427
| Erscheinungsdatum | 01.09.2022 |
|---|---|
| Reihe/Serie | Advances in Intelligent and Scientific Computing |
| Sprache | englisch |
| Maße | 10 x 10 mm |
| Gewicht | 454 g |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Informatik ► Office Programme ► Outlook | |
| ISBN-10 | 1-119-86498-4 / 1119864984 |
| ISBN-13 | 978-1-119-86498-1 / 9781119864981 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
aus dem Bereich
