(Studies In Big Data 26) Srinivasan S. (ed.) Guide To Applications Springer (2018)
(Studies%20in%20Big%20Data%2026)%20Srinivasan%20S.%20(ed.)-Guide%20to%20Big%20Data%20Applications-Springer%20(2018)
User Manual:
Open the PDF directly: View PDF
Page Count: 567 [warning: Documents this large are best viewed by clicking the View PDF Link!]
- Foreword
- Preface
- Acknowledgements
- Contents
- List of Reviewers
- Part I General
- 1 Strategic Applications of Big Data
- 1.1 Introduction
- 1.2 From Value Disciplines to Digital Disciplines
- 1.3 Information Excellence
- 1.4 Solution Leadership
- 1.4.1 Digital-Physical Mirroring
- 1.4.2 Real-Time Product/Service Optimization
- 1.4.3 Product/Service Usage Optimization
- 1.4.4 Predictive Analytics and Predictive Maintenance
- 1.4.5 Product-Service System Solutions
- 1.4.6 Long-Term Product Improvement
- 1.4.7 The Experience Economy
- 1.4.8 Experiences
- 1.4.9 Transformations
- 1.4.10 Customer-Centered Product and Service Data Integration
- 1.4.11 Beyond Business
- 1.5 Collective Intimacy
- 1.6 Accelerated Innovation
- 1.7 Integrated Disciplines
- 1.8 Conclusion
- References
- 2 Start with Privacy by Design in All Big Data Applications
- 3 Privacy Preserving Federated Big Data Analysis
- 4 Word Embedding for Understanding Natural Language:A Survey
- 1 Strategic Applications of Big Data
- Part II Applications in Science
- 5 Big Data Solutions to Interpreting Complex Systems in the Environment
- 6 High Performance Computing and Big Data
- 6.1 Introduction
- 6.2 High Performance in Action
- 6.3 High-Performance and Big Data Deployment Types
- 6.4 Software and Hardware Considerations for Building Highly Performant Data Platforms
- 6.5 Designing Data Pipelines for High Performance
- 6.6 Conclusions
- References
- 7 Managing Uncertainty in Large-Scale Inversions for the Oil and Gas Industry with Big Data
- 8 Big Data in Oil & Gas and Petrophysics
- 8.1 Introduction
- 8.2 The Value of Big Data for the Petroleum Industry
- 8.3 General Explanation of Terms
- 8.4 Steps to Big Data in the Oilfield
- 8.5 Future of Tools, Example
- 8.6 Next Step: Big Data Analytics in the Oil Industry
- 8.7 Big Data is the future of O&G
- 8.8 In Conclusion
- References
- 9 Friendship Paradoxes on Quora
- 9.1 Introduction
- 9.2 A Brief Review of the Statistics of Friendship Paradoxes: What are Strong Paradoxes, and Why Should We Measure Them?
- 9.2.1 Feld's Mathematical Argument
- 9.2.2 What Does Feld's Argument Imply?
- 9.2.3 Friendship Paradox Under Random Wiring
- 9.2.4 Beyond Random-Wiring Assumptions: Why Weak and Strong Friendship Paradoxes are Ubiquitous in Undirected Networks
- 9.2.5 Weak Generalized Paradoxes are Ubiquitous Too
- 9.2.6 Strong Degree-Based Paradoxes in Directed Networks and Strong Generalized Paradoxes are Nontrivial
- 9.3 Strong Paradoxes in the Quora Follow Network
- 9.4 A Strong Paradox in Downvoting
- 9.4.1 What are Upvotes and Downvotes?
- 9.4.2 The Downvoting Network and the Core Questions
- 9.4.3 The Downvoting Paradox is Absent in the Full Downvoting Network
- 9.4.4 The Downvoting Paradox Occurs When The Downvotee and Downvoter are Active Contributors
- 9.4.5 Does a ``Content-Contribution Paradox'' Explain the Downvoting Paradox?
- 9.4.6 Summary and Implications
- 9.5 A Strong Paradox in Upvoting
- 9.6 Conclusion
- References
- 10 Deduplication Practices for Multimedia Data in the Cloud
- 10.1 Context and Motivation
- 10.2 Data Deduplication Technical Design Issues
- 10.3 Chapter Highlights
- 10.4 Secure Image Deduplication Through Image Compression
- 10.5 Background
- 10.6 Proposed Image Deduplication Scheme
- 10.7 Secure Video Deduplication Scheme in Cloud Storage Environment Using H.264 Compression
- 10.8 Background
- 10.9 Proposed Video Deduplication Scheme
- 10.10 Chapter Summary
- References
- 11 Privacy-Aware Search and Computation Over Encrypted Data Stores
- 11.1 Introduction
- 11.2 Searchable Encryption Models
- 11.3 Text
- 11.4 Range Queries
- 11.5 Media
- 11.6 Other Applications
- 11.7 Conclusions
- References
- 12 Civil Infrastructure Serviceability Evaluation Based on Big Data
- 12.1 Introduction
- 12.2 Implementation Framework About MS-SHM-Hadoop
- 12.3 Acquisition of Sensory Data and Integration of Structure-Related Data
- 12.4 Nationwide Civil Infrastructure Survey
- 12.5 Global Structural Integrity Analysis
- 12.6 Localized Critical Component Reliability Analysis
- 12.7 Civil Infrastructure's Reliability Analysis Based on Bayesian Network
- 12.8 Conclusion and Future Work
- References
- Part III Applications in Medicine
- 13 Nonlinear Dynamical Systems with Chaos and Big Data:A Case Study of Epileptic Seizure Prediction and Control
- 13.1 Introduction
- 13.2 Background
- 13.3 Nonlinear Dynamical Systems with Chaos
- 13.4 Lyapunov Exponents
- 13.5 Rapid Prototyping HPCmatlab Platform
- 13.6 Case Study: Epileptic Seizure Prediction and Control
- 13.7 Future Research Opportunities and Conclusions
- Appendix 1: Electrical Stimulation and Experiment Setup
- Appendix 2: Preparation of Animals
- References
- 14 Big Data to Big Knowledge for Next Generation Medicine:A Data Science Roadmap
- 15 Time-Based Comorbidity in Patients Diagnosed with Tobacco Use Disorder
- 15.1 Introduction
- 15.2 Method
- 15.3 Results
- 15.3.1 Top 20 Diseases in TUD and Non-TUD Patients
- 15.3.2 Top 20 Diseases in TUD Patients and Corresponding Prevalence in Non-TUD Patients
- 15.3.3 Top 15 Comorbidities in TUD Patients Across Two Hospital Visits (Second Iteration) and Corresponding Prevalence in Non-TUD Patients
- 15.3.4 Comorbidities in TUD Patients Across Three Hospital Visits (Third Iteration) and Comparison with Non-TUD Patients
- 15.4 Discussion and Concluding Remarks
- References
- 16 The Impact of Big Data on the Physician
- 16.1 Part 1: The Patient-Physician Relationship
- 16.1.1 Defining Quality Care
- 16.1.2 Choosing the Best Doctor
- 16.1.3 Choosing the Best Hospital
- 16.1.4 Sharing Information: Using Big Data to Expand the Patient History
- 16.1.5 What is mHealth?
- 16.1.6 mHealth from the Provider Side
- 16.1.7 mHealth from the Patient Side
- 16.1.8 Logistical Concerns
- 16.1.9 Accessibility
- 16.1.10 Privacy and Security
- 16.1.11 Regulation and Liability
- 16.1.12 Patient Education and Partnering with Patients
- 16.1.13 Developing Online Health Communities Through Social Media: Creating Data that Fuels Research
- 16.1.14 Translating Complex Medical Datainto Patient-Friendly Formats
- 16.1.15 Beyond the Package Insert: Iodine.com
- 16.1.16 Data Inspires the Advent of New Models of Medical Care Delivery
- 16.2 Part II: Physician Uses for Big Data in Clinical Care
- References
- 16.1 Part 1: The Patient-Physician Relationship
- 13 Nonlinear Dynamical Systems with Chaos and Big Data:A Case Study of Epileptic Seizure Prediction and Control
- Part IV Applications in Business
- 17 The Potential of Big Data in Banking
- 18 Marketing Applications Using Big Data
- 19 Does Yelp Matter? Analyzing (And Guide to Using) Ratings for a Quick Serve Restaurant Chain
- Author Biographies
- Index