Big data analytics for intelligent healthcare management. (2019)

• Record Type: Book
• Further Information: Note: Volume editors, Nilanjan Dey, Himansu Das, Bighnaraj Naik, Himansu Sekhar Behera.
• Editors: Dey, Nilanjan; Das, Himansu; Naik, Bighnaraj; Behera, H. S
• 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
• Publisher Details: London : Academic Press
• Publication Date: 2019
• Extent: 1 online resource
• Subjects: 005.74; Computer science; Computer system failures; Database management; Data mining; Image processing; COMPUTERS / Databases / General; Electronic books
• Languages: English
• ISBNs: 9780128181478; 0128181478; 9780128181461
• Related ISBNs: 012818146X
• Notes: Note: Online resource; title from PDF title page (EBSCO, viewed April 17, 2019)
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.