Big Data Analytics

A: Modeling and Analytics

Chapter 1: Document Informatics for Scientific Learning and Accelerated Discovery

• Abstract
• 1 Introduction
• 2 How Document Informatics Will Aid Materials Discovery
• 3 The General Research Framework
• 4 Pilot Implementation

Chapter 2: An Introduction to Rare Event Simulation and Importance Sampling

• Abstract
• 1 Introduction: Monte Carlo Methods, Rare Event Simulation, and Variance Reduction Techniques
• 2 MC Methods and the Problem of Rare Events
• 3 Importance Sampling
• 4 Multiple IS
• 5 The Cross-Entropy Method
• 6 MCMC: Rejection Sampling, the Metropolis Method, and Gibbs Sampling
• 7 Applications of VRTs to Error Estimation in Optical Fiber Communication Systems
• 8 Large Deviations Theory, Asymptotic Efficiency, and Final Remarks

Chapter 3: A Large-Scale Study of Language Usage as a Cognitive Biometric Trait

• Abstract
• 1 Introduction
• 2 Cognitive Fingerprints: Problem Description
• 3 Data Description
• 4 Methodology
• 5 Results
• 6 Discussions
• 7 Related Work
• 8 Conclusions and Future Work
• Acknowledgment

Chapter 4: Customer Selection Utilizing Big Data Analytics

• Abstract
• 1 Introduction
• 2 Methodology
• 3 Experiments
• 4 Conclusion

Chapter 5: Continuous Model Selection for Large-Scale Recommender Systems

• Abstract
• 1 Introduction
• 2 Related Work
• 3 Preference Prediction
• 4 Proposed Continuous Modeling
• 5 Experimental Evaluations
• 6 Conclusion and Future Work

Chapter 6: Zero-Knowledge Mechanisms for Private Release of Social Graph Summarization

• Abstract
• 1 Introduction
• 2 Related Work
• 3 Graph Summarization
• 4 Background on ε-Zero-Knowledge Privacy
• 5 ZKP Mechanism for Graph Summarization
• 6 Evaluation
• 7 From Privacy Level to Noise Scale
• 8 Private Probabilistic A-GS
• 9 Conclusions

Chapter 7: Distributed Confidence-Weighted Classification on Big Data Platforms

• Abstract
• 1 Introduction
• 2 Classification with Linear SVM Models
• 3 MapReduce Framework for Distributed Computations
• 4 CW Classification Using MapReduce
• 5 Experiments
• 6 Conclusion
• Acknowledgments

B: Applications and Infrastructure

Chapter 8: Big Data Applications in Health Sciences and Epidemiology

• Abstract
• 1 Introduction
• 2 Mathematical Framework for Epidemiology
• 3 Dynamics and Analysis Problems
• 4 Inference Problems
• 5 Disease Surveillance, Molecular Epidemiology, and Pathogen Phylodynamics
• 6 High-Performance Synthetic Information Environments and Tools
• 7 Summary
• Acknowledgments

Chapter 9: Big Data Driven Natural Language Processing Research and Applications

• Abstract
• 1 Introduction
• 2 NLP Core Tasks
• 3 NLP Applications
• 4 Data Sources for NLP Research
• 5 Big Data Driven NLP Research and Applications
• 6 Trends and Future Research Directions
• 7 Conclusions

Chapter 10: Analyzing Big Spatial and Big Spatiotemporal Data: A Case Study of Methods and Applications

• Abstract
• 1 Introduction
• 2 Algorithms
• 3 Applications
• 4 Conclusions

Chapter 11: Experimental Computational Simulation Environments for Big Data Analytic in Social Sciences

• Abstract
• 1 Introduction
• 2 Big Data Analytics
• 3 Sociofinancial-Economic Simulations
• 4 Software Infrastructure for Social Sciences
• 5 Market Simulators for Financial Economics Modeling
• 6 Statistical Simulations of AT Models
• 7 DRACUS
• 8 Summary

Chapter 12: Terabyte-Scale Image Similarity Search

• Abstract
• 1 Introduction
• 2 Big-Data Processing
• 3 Application Workload (Distributed Indexing + Searching)
• 4 Hadoop in Practice
• 5 Large-Scale Hadoop
• 6 Conclusion
• Acknowledgments

Chapter 13: Measuring Inter-site Engagement in a Network of Sites

• Abstract
• 1 Introduction
• 2 Related Work
• 3 Data, Networks, and Metrics
• 4 Evaluating Inter-site Metrics
• 5 Studying Inter-site Engagement
• 6 The Network Effect
• 7 Hyperlink Performance
• 8 Conclusions
• 9 Future Work
• Acknowledgments

Chapter 14: Scaling RDF Triple Stores in Size and Performance: Modeling SPARQL Queries as Graph Homomorphism Routines

• Abstract
• 1 Introduction
• 2 SPARQL Queries as Graph Homomorphism Routines
• 3 GEMS: Graph Database Engine for Multithreaded Systems
• 4 Related Work
• 5 Experimental Results
• 6 Conclusions