Coverart for item
The Resource Applications of big data analytics : trends, issues and challenges, Mohammed M. Alani [and 3 others], editors

Applications of big data analytics : trends, issues and challenges, Mohammed M. Alani [and 3 others], editors

Label
Applications of big data analytics : trends, issues and challenges
Title
Applications of big data analytics
Title remainder
trends, issues and challenges
Statement of responsibility
Mohammed M. Alani [and 3 others], editors
Contributor
Editor
Subject
Language
eng
Summary
This text/reference reviews the state of the art of big data analytics, with a particular focus on practical applications. An authoritative selection of leading international researchers present detailed analyses of existing trends for storing and analyzing big data, together with valuable insights into the challenges inherent in current approaches and systems. This is further supported by real-world examples drawn from a broad range of application areas, including healthcare, education, and disaster management. The text also covers, typically from an application-oriented perspective, advances in data science in such areas as big data collection, searching, analysis, and knowledge discovery
Member of
Cataloging source
N$T
Dewey number
005.7
Index
no index present
LC call number
QA76.9.B45
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorName
Alani, Mohammed M.
http://library.link/vocab/subjectName
  • Big data
  • Medical care
  • Nuclear power plants
  • Education
Label
Applications of big data analytics : trends, issues and challenges, Mohammed M. Alani [and 3 others], editors
Instantiates
Publication
Antecedent source
unknown
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Intro; Preface; Organization of the Book; Contents; 1 Big Data Environment for Smart Healthcare Applications Over 5G Mobile Network; 1.1 Introduction; 1.1.1 Smart Devices; 1.1.2 Future Challenges; 1.2 Background; 1.2.1 5G Enabling Technologies; 1.2.2 Infrastructure-Based RNs; 1.2.2.1 Fixed Relay Nodes; 1.2.2.2 Mobile Relay Nodes; 1.2.3 5G Network Slicing; 1.2.3.1 Data Traffic Aggregation Model; 1.2.4 Resource Allocation Scheme (RAS); 1.3 Resource Allocation Scheme Environment; 1.3.1 Related Works; 1.3.2 System Models; 1.3.2.1 Service Slices; 1.3.2.2 Virtual Network; 1.3.2.3 Physical Resources
  • 1.3.3 Two-Tier Scheme and Resource Allocation1.3.3.1 Services Allocation; 1.3.3.2 Service Slices Strategy; 1.3.3.3 Resource Allocation; 1.4 Simulation Approach; 1.4.1 Simulation Setup; 1.4.2 QoS of Radio Bearers; 1.4.3 Radio Resource Allocation Algorithm; 1.5 Simulation Scenarios; 1.5.1 OPNET 5G Model Description; 1.5.2 Experimental Results; 1.6 Conclusion; References; 2 Challenges and Opportunities of Using Big Data for Assessing Flood Risks; 2.1 Introduction; 2.2 Impact of Flood as a Natural Disaster; 2.3 Big Data for Flood Risk Management; 2.3.1 How Can Big Data Help?
  • 2.4 Opportunities of Big Data in Flood Risk Assessment2.5 Challenges of Predicting Flood Risks; 2.6 System Architecture Implementing Big Data; 2.6.1 Framework of the Assessment Model; 2.7 Current Research on Flood Prediction Using Big Data; 2.8 Conclusion; References; 3 A Neural Networks Design Methodology for Detecting Loss of Coolant Accidents in Nuclear Power Plants; 3.1 Introduction; 3.2 Approaches for Monitoring the Safety of Nuclear Power Plants; 3.3 Large Break Loss of Coolant Accidents of a PHWR; 3.4 The Neural Networks Training Methodology; 3.4.1 Performance Measures
  • 3.4.2 Random Data Split and Normalisation of the Transient Dataset3.4.3 Training of 1-Hidden Layer MLPs and Selection of the Optimised 1-Hidden Layer MLP; 3.4.4 Training of 2-Hidden Layer MLPs and Selection of the Optimised 2-Hidden Layer MLP; 3.4.5 Training the Optimised 2-Hidden Layer MLP on Linear Interpolation Dataset and Transient Dataset; 3.5 Results; 3.5.1 The Optimised 1-Hidden Layer MLP; 3.5.2 The Optimised 2-Hidden Layer MLP; 3.5.3 Training the Optimised 2-Hidden Layer MLP on Linear Interpolation Dataset and Transient Dataset
  • 3.5.4 Performance Comparison with the Neural Network of the Previous Work3.5.5 Performance Comparison with Exhaustive Training of All 2-Hidden Layer Architectures; 3.6 Discussion; 3.7 Conclusion; References; 4 Evolutionary Deployment and Hill Climbing-Based Movements of Multi-UAV Networks in Disaster Scenarios; 4.1 Introduction; 4.2 Related Work; 4.2.1 Deployment Problem; 4.2.2 Mobility Models for Disaster Scenarios; 4.3 Modeling Disaster Scenarios; 4.3.1 Disaster Scenario Layout; 4.3.2 Mobility of Victims; 4.3.3 0th Responders; 4.3.4 Communications in Disaster Scenarios
Dimensions
unknown
Extent
1 online resource.
File format
unknown
Form of item
online
Isbn
9783319764719
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
10.1007/978-3-319-76472-6
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • on1046634135
  • (OCoLC)1046634135
Label
Applications of big data analytics : trends, issues and challenges, Mohammed M. Alani [and 3 others], editors
Publication
Antecedent source
unknown
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
  • Intro; Preface; Organization of the Book; Contents; 1 Big Data Environment for Smart Healthcare Applications Over 5G Mobile Network; 1.1 Introduction; 1.1.1 Smart Devices; 1.1.2 Future Challenges; 1.2 Background; 1.2.1 5G Enabling Technologies; 1.2.2 Infrastructure-Based RNs; 1.2.2.1 Fixed Relay Nodes; 1.2.2.2 Mobile Relay Nodes; 1.2.3 5G Network Slicing; 1.2.3.1 Data Traffic Aggregation Model; 1.2.4 Resource Allocation Scheme (RAS); 1.3 Resource Allocation Scheme Environment; 1.3.1 Related Works; 1.3.2 System Models; 1.3.2.1 Service Slices; 1.3.2.2 Virtual Network; 1.3.2.3 Physical Resources
  • 1.3.3 Two-Tier Scheme and Resource Allocation1.3.3.1 Services Allocation; 1.3.3.2 Service Slices Strategy; 1.3.3.3 Resource Allocation; 1.4 Simulation Approach; 1.4.1 Simulation Setup; 1.4.2 QoS of Radio Bearers; 1.4.3 Radio Resource Allocation Algorithm; 1.5 Simulation Scenarios; 1.5.1 OPNET 5G Model Description; 1.5.2 Experimental Results; 1.6 Conclusion; References; 2 Challenges and Opportunities of Using Big Data for Assessing Flood Risks; 2.1 Introduction; 2.2 Impact of Flood as a Natural Disaster; 2.3 Big Data for Flood Risk Management; 2.3.1 How Can Big Data Help?
  • 2.4 Opportunities of Big Data in Flood Risk Assessment2.5 Challenges of Predicting Flood Risks; 2.6 System Architecture Implementing Big Data; 2.6.1 Framework of the Assessment Model; 2.7 Current Research on Flood Prediction Using Big Data; 2.8 Conclusion; References; 3 A Neural Networks Design Methodology for Detecting Loss of Coolant Accidents in Nuclear Power Plants; 3.1 Introduction; 3.2 Approaches for Monitoring the Safety of Nuclear Power Plants; 3.3 Large Break Loss of Coolant Accidents of a PHWR; 3.4 The Neural Networks Training Methodology; 3.4.1 Performance Measures
  • 3.4.2 Random Data Split and Normalisation of the Transient Dataset3.4.3 Training of 1-Hidden Layer MLPs and Selection of the Optimised 1-Hidden Layer MLP; 3.4.4 Training of 2-Hidden Layer MLPs and Selection of the Optimised 2-Hidden Layer MLP; 3.4.5 Training the Optimised 2-Hidden Layer MLP on Linear Interpolation Dataset and Transient Dataset; 3.5 Results; 3.5.1 The Optimised 1-Hidden Layer MLP; 3.5.2 The Optimised 2-Hidden Layer MLP; 3.5.3 Training the Optimised 2-Hidden Layer MLP on Linear Interpolation Dataset and Transient Dataset
  • 3.5.4 Performance Comparison with the Neural Network of the Previous Work3.5.5 Performance Comparison with Exhaustive Training of All 2-Hidden Layer Architectures; 3.6 Discussion; 3.7 Conclusion; References; 4 Evolutionary Deployment and Hill Climbing-Based Movements of Multi-UAV Networks in Disaster Scenarios; 4.1 Introduction; 4.2 Related Work; 4.2.1 Deployment Problem; 4.2.2 Mobility Models for Disaster Scenarios; 4.3 Modeling Disaster Scenarios; 4.3.1 Disaster Scenario Layout; 4.3.2 Mobility of Victims; 4.3.3 0th Responders; 4.3.4 Communications in Disaster Scenarios
Dimensions
unknown
Extent
1 online resource.
File format
unknown
Form of item
online
Isbn
9783319764719
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other control number
10.1007/978-3-319-76472-6
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • on1046634135
  • (OCoLC)1046634135

Library Locations

Processing Feedback ...