| Autore |
Raj Pethuru
|
| Edizione | [1st ed.] |
| Pubbl/distr/stampa |
Stevenage : , : Institution of Engineering & Technology, , 2022
|
| Descrizione fisica |
1 online resource (363 pages)
|
| Disciplina |
511.5
|
| Altri autori (Persone) |
KumarAbhishek
García DíazVicente
Muthuraman SundarNachamai
|
| Collana |
Computing and Networks
|
| Soggetto topico |
Graph algorithms
|
| ISBN |
1-83724-484-7
1-5231-5341-5
1-83953-489-3
|
| Formato |
Materiale a stampa  |
| Livello bibliografico |
Monografia |
| Lingua di pubblicazione |
eng
|
| Nota di contenuto |
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.
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.
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.
9.3 Various working techniques in graph database.
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| Record Nr. | UNINA-9911004860003321 |
Info
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