Coverart for item
The Resource Big data analytics for intelligent healthcare management, volume editors, Nilanjan Dey, Himansu Das, Bighnaraj Naik, Himansu Sekhar Behera

Big data analytics for intelligent healthcare management, volume editors, Nilanjan Dey, Himansu Das, Bighnaraj Naik, Himansu Sekhar Behera

Label
Big data analytics for intelligent healthcare management
Title
Big data analytics for intelligent healthcare management
Statement of responsibility
volume editors, Nilanjan Dey, Himansu Das, Bighnaraj Naik, Himansu Sekhar Behera
Contributor
Editor
Subject
Language
eng
Summary
Big Data Analytics for Intelligent Healthcare Management covers both the theory and application of hardware platforms and architectures, the development of software methods, techniques and tools, applications and governance, and adoption strategies for the use of big data in healthcare and clinical research. The book provides the latest research findings on the use of big data analytics with statistical and machine learning techniques that analyze huge amounts of real-time healthcare data
Member of
Cataloging source
N$T
Dewey number
005.74
Index
no index present
LC call number
QA76.9.D3
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorName
  • Dey, Nilanjan
  • Das, Himansu
  • Naik, Bighnaraj
  • Behera, H. S.
http://library.link/vocab/subjectName
  • Computer science
  • Computer system failures
  • Database management
  • Data mining
  • Image processing
Label
Big data analytics for intelligent healthcare management, volume editors, Nilanjan Dey, Himansu Das, Bighnaraj Naik, Himansu Sekhar Behera
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
  • Front Cover; Big Data Analytics for Intelligent Healthcare Management; Copyright; Contents; Contributors; Preface; Acknowledgments; Chapter 1: Bio-Inspired Algorithms for Big Data Analytics: A Survey, Taxonomy, and Open Challenges; 1.1. Introduction; 1.1.1. Dimensions of Data Management; 1.2. Big Data Analytical Model; 1.3. Bio-Inspired Algorithms for Big Data Analytics: A Taxonomy; 1.3.1. Evolutionary Algorithms; 1.3.2. Swarm-Based Algorithms; 1.3.3. Ecological Algorithms; 1.3.4. Discussions; 1.4. Future Research Directions and Open Challenges; 1.4.1. Resource Scheduling and Usability
  • 1.4.2. Data Processing and Elasticity1.4.3. Resilience and Heterogeneity in Interconnected Clouds; 1.4.4. Sustainability and Energy-Efficiency; 1.4.5. Data Security and Privacy Protection; 1.4.6. IoT-Based Edge Computing and Networking; 1.5. Emerging Research Areas in Bio-Inspired Algorithm-Based Big Data Analytics; 1.5.1. Container as a Service (CaaS); 1.5.2. Serverless Computing as a Service (SCaaS); 1.5.3. Blockchain as a Service (BaaS); 1.5.4. Software-defined Cloud as a Service (SCaaS); 1.5.5. Deep Learning as a Service (DLaaS); 1.5.6. Bitcoin as a Service (BiaaS)
  • 1.5.7. Quantum Computing as a Service (QCaaS)1.6. Summary and Conclusions; Acknowledgments; References; Further Reading; Chapter 2: Big Data Analytics Challenges and Solutions; 2.1. Introduction; 2.1.1. Consumable Massive Facts Analytics; 2.1.2. Allotted Records Mining Algorithms; 2.1.3. Gadget Failure; 2.1.4. Facts Aggregation Challenges; 2.1.5. Statistics Preservation-Demanding Situations; 2.1.6. Information Integration Challenges; 2.2. Records Analysis Challenges; 2.2.1. Scale of the Statistics; 2.2.2. Pattern Interpretation Challenges; 2.3. Arrangements of Challenges
  • 2.3.1. User Intervention Method2.3.2. Probabilistic Method; 2.3.3. Defining and Detecting Anomalies in Human Ecosystems; 2.4. Demanding Situations in Managing Huge Records; 2.5. Massive Facts Equal Large Possibilities; 2.5.1. Present Answers to Challenges for the Quantity Mission; 2.5.1.1. Hadoop; 2.5.1.2. Hadoop-distributed file system; 2.5.1.3. Hadoop MapReduce; 2.5.1.4. Apache spark; 2.5.1.5. Grid computing; 2.5.1.6. Spark structures; 2.5.1.7. Capacity solutions for records-variety trouble; 2.5.2. Image Mining and Processing With Big Data; 2.5.3. Potential Answers for Velocity Trouble
  • 2.5.3.1. Transactional databases2.5.3.2. Statistics representation; 2.5.3.3. Massive actualities calculations; 2.5.3.4. Ability solutions for privateers and safety undertaking; 2.5.4. Ability Solutions for Scalability Assignments; 2.5.4.1. Big data and cloud computing; 2.5.4.2. Cloud computing service models; 2.5.4.3. Answers; 2.5.4.4. Use record encryption; 2.5.4.5. Imposing access controls; 2.5.4.6. Logging; 2.6. Discussion; 2.7. Conclusion; Glossary; References; Further Reading; Chapter 3: Big Data Analytics in Healthcare: A Critical Analysis; 3.1. Introduction; 3.2. Big Data
Dimensions
unknown
Extent
1 online resource.
File format
unknown
Form of item
online
Isbn
9780128181478
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Note
Online access with subscription: Elsevier (Sciencedirect Freedom Collection)
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • on1097248476
  • (OCoLC)1097248476
Label
Big data analytics for intelligent healthcare management, volume editors, Nilanjan Dey, Himansu Das, Bighnaraj Naik, Himansu Sekhar Behera
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
  • Front Cover; Big Data Analytics for Intelligent Healthcare Management; Copyright; Contents; Contributors; Preface; Acknowledgments; Chapter 1: Bio-Inspired Algorithms for Big Data Analytics: A Survey, Taxonomy, and Open Challenges; 1.1. Introduction; 1.1.1. Dimensions of Data Management; 1.2. Big Data Analytical Model; 1.3. Bio-Inspired Algorithms for Big Data Analytics: A Taxonomy; 1.3.1. Evolutionary Algorithms; 1.3.2. Swarm-Based Algorithms; 1.3.3. Ecological Algorithms; 1.3.4. Discussions; 1.4. Future Research Directions and Open Challenges; 1.4.1. Resource Scheduling and Usability
  • 1.4.2. Data Processing and Elasticity1.4.3. Resilience and Heterogeneity in Interconnected Clouds; 1.4.4. Sustainability and Energy-Efficiency; 1.4.5. Data Security and Privacy Protection; 1.4.6. IoT-Based Edge Computing and Networking; 1.5. Emerging Research Areas in Bio-Inspired Algorithm-Based Big Data Analytics; 1.5.1. Container as a Service (CaaS); 1.5.2. Serverless Computing as a Service (SCaaS); 1.5.3. Blockchain as a Service (BaaS); 1.5.4. Software-defined Cloud as a Service (SCaaS); 1.5.5. Deep Learning as a Service (DLaaS); 1.5.6. Bitcoin as a Service (BiaaS)
  • 1.5.7. Quantum Computing as a Service (QCaaS)1.6. Summary and Conclusions; Acknowledgments; References; Further Reading; Chapter 2: Big Data Analytics Challenges and Solutions; 2.1. Introduction; 2.1.1. Consumable Massive Facts Analytics; 2.1.2. Allotted Records Mining Algorithms; 2.1.3. Gadget Failure; 2.1.4. Facts Aggregation Challenges; 2.1.5. Statistics Preservation-Demanding Situations; 2.1.6. Information Integration Challenges; 2.2. Records Analysis Challenges; 2.2.1. Scale of the Statistics; 2.2.2. Pattern Interpretation Challenges; 2.3. Arrangements of Challenges
  • 2.3.1. User Intervention Method2.3.2. Probabilistic Method; 2.3.3. Defining and Detecting Anomalies in Human Ecosystems; 2.4. Demanding Situations in Managing Huge Records; 2.5. Massive Facts Equal Large Possibilities; 2.5.1. Present Answers to Challenges for the Quantity Mission; 2.5.1.1. Hadoop; 2.5.1.2. Hadoop-distributed file system; 2.5.1.3. Hadoop MapReduce; 2.5.1.4. Apache spark; 2.5.1.5. Grid computing; 2.5.1.6. Spark structures; 2.5.1.7. Capacity solutions for records-variety trouble; 2.5.2. Image Mining and Processing With Big Data; 2.5.3. Potential Answers for Velocity Trouble
  • 2.5.3.1. Transactional databases2.5.3.2. Statistics representation; 2.5.3.3. Massive actualities calculations; 2.5.3.4. Ability solutions for privateers and safety undertaking; 2.5.4. Ability Solutions for Scalability Assignments; 2.5.4.1. Big data and cloud computing; 2.5.4.2. Cloud computing service models; 2.5.4.3. Answers; 2.5.4.4. Use record encryption; 2.5.4.5. Imposing access controls; 2.5.4.6. Logging; 2.6. Discussion; 2.7. Conclusion; Glossary; References; Further Reading; Chapter 3: Big Data Analytics in Healthcare: A Critical Analysis; 3.1. Introduction; 3.2. Big Data
Dimensions
unknown
Extent
1 online resource.
File format
unknown
Form of item
online
Isbn
9780128181478
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Note
Online access with subscription: Elsevier (Sciencedirect Freedom Collection)
Quality assurance targets
not applicable
Reformatting quality
unknown
Sound
unknown sound
Specific material designation
remote
System control number
  • on1097248476
  • (OCoLC)1097248476

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