Demystifying Graph Data Science Graph algorithms, analytics methods, platforms, databases, and use cases

With the growing maturity and stability of digitization and edge technologies, vast numbers of digital entities, connected devices, and microservices interact purposefully to create huge sets of poly-structured digital data. Corporations are continuously seeking fresh ways to use their data to drive...

Full description

Saved in:
Bibliographic Details
Main Authors Raj, Pethuru, Kumar, Abhishek, García Díaz, Vicente, Muthuraman, Nachamai
Format eBook
LanguageEnglish
Published Stevenage The Institution of Engineering and Technology 2022
Institution of Engineering and Technology (The IET)
Institution of Engineering & Technology
Institution of Engineering and Technology
Edition1
SeriesIET computing series
Subjects
Computer networking & communications
COMPUTERS
Data capture & analysis
Database Management
General References
Graph theory
Networking
Software Engineering
Online AccessGet full text
ISBN1839534885
9781839534881
DOI10.1049/PBPC048E

Cover

Table of Contents:
  • Chapter 1: Toward graph data science -- Chapter 2: Data science: the Artificial Intelligence (AI) algorithms-inspired use cases -- Chapter 3: Accelerating graph analytics -- Chapter 4: Introduction to IoT data analytics and its use cases -- Chapter 5: Demystifying digital transformation technologies in healthcare -- Chapter 6: Semantic knowledge graph technologies in data science -- Chapter 7: Why graph analytics? -- Chapter 8: Graph technology: a detailed study of trending techniques and technologies of graph analytics -- Chapter 9: A holistic analysis to identify the efficiency of data growth using a standardized method of non-functional requirements in graph applications -- Chapter 10: Roadmap of integrated data analytics - practices, business strategies and approaches -- Chapter 11: Introduction to graph analytics -- Chapter 12: A study of graph analytics for massive datasets -- Chapter 13: Demystifying graph AI -- Chapter 14: Application of graph data science and graph databases in major industries -- Chapter 15: Graph data science for cybersecurity -- Chapter 16: The machine learning algorithms for data science applications
  • Title Page Book Preface Table of Contents 1. Toward Graph Data Science 2. Data Science: The Artificial Intelligence (AI) Algorithms-Inspired Use Cases 3. Accelerating Graph Analytics 4. Introduction to IoT Data Analytics and its Use Cases 5. Demystifying Digital Transformation Technologies in Healthcare 6. Semantic Knowledge Graph Technologies in Data Science 7. Why Graph Analytics? 8. Graph Technology: A Detailed Study of Trending Techniques and Technologies of Graph Analytics 9. A Holistic Analysis to Identify the Efficiency of Data Growth Using a Standardized Method of Nonfunctional Requirements in Graph Applications 10. Roadmap of Integrated Data Analytics - Practices, Business Strategies and Approaches 11. Introduction to Graph Analytics 12. A Study of Graph Analytics for Massive Datasets 13. Demystifying Graph AI 14. Application of Graph Data Science and Graph Databases in Major Industries 15. Graph Data Science for Cybersecurity 16. The Machine Learning Algorithms for Data Science Applications Index
  • 4.4 AI -- 4.4.1 Training strategies -- 4.4.2 ML -- 4.4.3 Decision tree (DT) and random forest (RF) -- 4.4.4 Support vector machine (SVM) -- 4.4.5 DL -- 4.4.6 Convolutional neural networks (CNN) -- 4.4.7 Architectures and implementations -- 4.4.8 Other neural networks -- 4.5 Orchestration -- 4.5.1 Container technologies -- 4.5.2 Overview -- 4.5.3 Architecture -- 4.5.4 Cluster constructs -- 4.5.5 Deploying a service -- 4.5.6 Containerizing the application -- 4.5.7 Deployment -- 4.5.8 Microservices -- 4.6 IoT deployments -- 4.6.1 Edge deployments -- 4.6.2 Hybrid cloud edge deployments -- References -- 5 Demystifying digital transformation technologies in healthcare -- 5.1 Introduction -- 5.2 Primal elements driving the medical industry -- 5.2.1 Recent research in healthcare -- 5.2.2 Medical expenses and their surge -- 5.2.3 Improvement in mortality rate of older people -- 5.2.4 Modulating relationship -- 5.2.5 Eccentric frameworks -- 5.3 Technology trends in healthcare -- 5.3.1 Smart watches and clinical device network -- 5.3.2 Intelligence and data analytics -- 5.3.3 Augmented reality (AR) and virtual reality (VR) -- 5.3.4 Telemedicine -- 5.4 Technology challenges in healthcare -- 5.4.1 Data processing -- 5.4.2 Cybersecurity -- 5.4.3 Digital user experience -- 5.5 Big Data in healthcare -- 5.6 Big Data in healthcare applications -- 5.7 Challenges for Big Data in healthcare -- 5.7.1 Data collection challenges -- 5.7.2 Procedure and method challenges -- 5.7.3 Data management challenges -- 5.7.4 Significant factors that support health plan agencies in enhancing quality measurement results -- 5.8 Case study -- 5.9 Proposed method -- 5.10 Experimental results and discussion -- 5.10.1 Accuracy analysis -- 5.10.2 Average performance analysis -- 5.10.3 Average response time (ART) -- 5.10.4 Pattern classification time -- 5.10.5 Error rate -- 5.10.6 Conclusion
  • 9.3 Various working techniques in graph database
  • References -- 6 Semantic knowledge graph technologies in data science -- 6.1 Introduction -- 6.2 Knowledge extraction and information extraction -- 6.2.1 Professional-based systems -- 6.2.2 Construction of knowledge graph -- 6.2.3 Conceptualization -- 6.3 Creating knowledge graphs using semantic models -- 6.3.1 System construction -- 6.3.2 Synopsis mapping -- 6.3.3 Semantic model -- 6.4 Semantic graph infrastructure -- 6.4.1 Sources of knowledge -- 6.4.2 Extraction of knowledge -- 6.4.3 Convergence of knowledge -- 6.4.4 Processing, collection, and graphical demonstration of knowledge graphs -- 6.5 Semantic knowledge graph -- 6.5.1 Architecture -- 6.5.2 Characteristics -- 6.5.3 Evaluations -- 6.6 Finance industry - a case study for knowledge graph -- 6.6.1 Data authority -- 6.6.2 Automated fraud detection -- 6.6.3 Knowledge management -- 6.6.4 Insider trading -- 6.6.5 AI in capital funding -- 6.6.6 Enabling venture capitalization -- 6.6.7 Analyzing credentials -- 6.6.8 Product-based community analysis -- 6.6.9 Challenges -- 6.7 Conclusion -- References -- 7 Why graph analytics? -- 7.1 Introduction -- 7.1.1 Types of graphs -- 7.1.2 Difference between relational analytics and graph analytics -- 7.2 Big graph analytics -- 7.2.1 Vs of big graph -- 7.3 The basics of graph analytics -- 7.4 Graph analytic techniques -- 7.4.1 Path analytic -- 7.4.2 Analytical connectivity -- 7.4.3 Community analytic -- 7.4.4 Centrality analytic -- 7.5 Big graph analytics algorithms -- 7.5.1 PageRank -- 7.5.2 Connected component -- 7.5.3 Distributed minimum spanning tree -- 7.5.4 Graph search -- 7.5.5 Clustering -- 7.6 Big graph analytics framework for healthcare -- 7.6.1 Big graph characteristics -- 7.6.2 Impact of big graph analytics in healthcare -- 7.6.3 Proposed framework for patient data analytics -- 7.6.4 Key players of proposed model -- 7.7 Implementation and results
  • 7.8 Conclusion -- References -- 8 Graph technology: a detailed study of trending techniques and technologies of graph analytics -- 8.1 Introduction -- 8.1.1 Centrality -- 8.1.2 Degree centrality -- 8.1.3 Types of graphs -- 8.1.4 Graph algorithms and graph analytics implementations -- 8.1.5 Page rank -- 8.1.6 Graph database -- 8.1.7 Applications -- 8.1.8 Major graph analytics applications -- 8.2 Technology for graph databases -- 8.2.1 Enactment -- 8.2.2 Flexibility -- 8.2.3 Agility -- 8.3 Graph data science? -- 8.3.1 Graph data science applications -- 8.3.2 Graph data science library -- 8.4 Defined graph database -- 8.4.1 Forms of graph databases -- 8.4.2 Graphs of properties -- 8.4.3 Graphs in RDF -- 8.4.4 What are graphs and graph libraries and how do they work? -- 8.4.5 Benefits in graph databases -- 8.4.6 What is the role of graph databases and graph analytics? -- 8.4.7 Graph database use case: money laundering -- 8.4.8 Social network review is a good example of how to use a graph database -- 8.4.9 Credit card theft is an example of how a graph database can be used -- 8.4.10 The evolution of graph databases -- 8.5 Graph analytics trending techniques and technologies -- 8.5.1 Trend 1: Artificial intelligence (AI) that is smarter, quicker, and more accountable -- 8.5.2 Trend 2 -- 8.5.3 The third trend is decision intelligence -- 8.5.4 Trend 4th: X analytics -- 8.5.5 Trend 5th: enhanced data protection -- 8.6 Conclusion -- References -- 9 A holistic analysis to identify the efficiency of data growth using a standardized method of non-functional requirements in graph applications -- 9.1 Introduction -- 9.1.1 Key features of graph database -- 9.1.2 Process related to graph database -- 9.2 Literature survey -- 9.2.1 Graph data modeling -- 9.2.2 Non-functional requirements -- 9.2.3 Building of graph-based application model
  • Intro -- Title -- Copyright -- Contents -- About the Editors -- Book preface -- 1 Toward graph data science -- 1.1 Introduction -- 1.2 Concept of graph -- 1.3 Graph travels on analysis -- 1.4 Graph plotting -- 1.5 Network graph of an ETFL ARK Funds -- 1.6 Twitch verse -- 1.6.1 Use of graph theory mechanisms for solving data science problems -- 1.7 Data visualization techniques -- 1.8 Present research ongoing -- 1.9 Next 60 years of data science -- 1.10 Scientific data analytics tested empirically -- 1.11 Conclusion -- 1.12 Future work -- References -- 2 Data science: the Artificial Intelligence (AI) algorithms-inspired use cases -- 2.1 Introduction -- 2.2 The evolution and elevation of data science -- 2.3 Anomaly detection -- 2.3.1 Binary and multiclass classification -- 2.3.2 Personalization -- 2.4 Fraud detection -- 2.4.1 Challenges to fraud detection -- 2.4.2 Best practices for observability with fraud models -- 2.4.3 Important metrics -- 2.4.4 Performance degradation -- 2.4.5 Overcoming the drift problem -- 2.5 AI-enabled fake news detection -- 2.6 AI-inspired credit card fraud detection -- 2.7 AI-empowered forest fire prediction -- 2.8 AI-induced breast cancer (BC) detection -- 2.8.1 Phase 0 - preparation of data -- 2.8.2 Phase 1: data investigation -- 2.8.3 Phase 2: data categories -- 2.8.4 Phase 3: feature scaling -- 2.8.5 Phase 4: ML model selection -- 2.8.6 Phase 5 - model evaluation -- 2.8.7 Phase 6 - model optimization -- 2.9 Stopping cyber attacks by AI algorithms -- 2.10 ML for cyber security -- 2.11 Network protection -- 2.12 Endpoint detection and response (EDR) -- 2.13 Threat detection by EDR -- 2.14 Containment -- 2.15 Application security -- 2.16 User behavior -- 2.17 Process behavior -- 2.18 The modern data architecture (MDA) -- 2.18.1 Smart applications -- 2.18.2 Smarter edge
  • 2.18.3 Faster, more accurate, and easier management -- 2.19 The Kafka platform for data scientists -- 2.20 Kafka APIs -- 2.21 Conclusion -- References -- 3 Accelerating graph analytics -- 3.1 Introduction -- 3.2 Graph analytics methods to deliver smarter AI -- 3.2.1 Semi supervised learning with graph algorithms -- 3.3 Data preparation -- 3.4 Steps to get started with graph machine learning model -- 3.4.1 Structured query-oriented knowledge graphs -- 3.4.2 Query-based feature engineering -- 3.4.3 Extending the use of graph algorithms -- 3.4.4 Approaches based on local similarity -- 3.4.5 Approaches that are based on global similarity -- 3.4.6 Approaches based on quasi-local similarity -- 3.5 Graph embeddings -- 3.5.1 Why graph embeddings are needed? -- 3.5.2 GNN and native learning -- 3.5.3 Based on the graph type -- 3.6 Applications -- 3.6.1 Classification of text -- 3.6.2 Translation by a neural computer -- 3.6.3 Image classification is a technique used in the field of image manipulation -- 3.6.4 Object detection is a feature that allows detecting of objects in the environment -- 3.6.5 Semantic segmentation is the process of separating words based on their semantic meaning -- 3.6.6 Combinatorial optimization is a technique for maximizing the number of options -- 3.7 Conclusion -- References -- 4 Introduction to IoT data analytics and its use cases -- 4.1 Background and context -- 4.1.1 Cost of compute -- 4.1.2 AI/ML frameworks and optimizations -- 4.1.3 Dedicated hardware components -- 4.1.4 Sensors and data -- 4.1.5 Pre-trained models -- 4.1.6 Retail -- 4.1.7 Medical -- 4.1.8 Industrial -- 4.1.9 Automotive -- 4.1.10 Education -- 4.1.11 Conclusion -- 4.2 IoT analytics system and concepts -- 4.2.1 Overview -- 4.2.2 Data ingestion -- 4.2.3 Analytics pipeline -- 4.3 Network -- 4.3.1 Wired network technologies -- 4.3.2 Wireless network technologies