Big Data Analytics, Volume 33

• Preface
• 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
• Index

While the term Big Data is open to varying interpretation, it is quite clear that the Volume, Velocity, and Variety (3Vs) of data have impacted every aspect of computational science and its applications. The volume of data is increasing at a phenomenal rate and a majority of it is unstructured. With big data, the volume is so large that processing it using traditional database and software techniques is difficult, if not impossible. The drivers are the ubiquitous sensors, devices, social networks and the all-pervasive web. Scientists are increasingly looking to derive insights from the massive quantity of data to create new knowledge. In common usage, Big Data has come to refer simply to the use of predictive analytics or other certain advanced methods to extract value from data, without any required magnitude thereon. Challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and information privacy. While there are challenges, there are huge opportunities emerging in the fields of Machine Learning, Data Mining, Statistics, Human-Computer Interfaces and Distributed Systems to address ways to analyze and reason with this data. The edited volume focuses on the challenges and opportunities posed by "Big Data" in a variety of domains and how statistical techniques and innovative algorithms can help glean insights and accelerate discovery. Big data has the potential to help companies improve operations and make faster, more intelligent decisions.

• Review of big data research challenges from diverse areas of scientific endeavor
• Rich perspective on a range of data science issues from leading researchers
• Insight into the mathematical and statistical theory underlying the computational methods used to address big data analytics problems in a variety of domains

Computer scientists, statisticians, data scientists, and Artificial Intelligence researchers