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Big data analytics /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Amsterdam :
Elsevier,
2015.
|
| Colección: | Handbook of statistics (Amsterdam, Netherlands) ;
v. 33. |
| Temas: | |
| Acceso en línea: | Texto completo Texto completo |
Tabla de Contenidos:
- Front Cover; Big Data Analytics; Copyright; Contents; Contributors; Preface; Part A: Modeling and Analytics; Chapter 1: Document Informatics for Scientific Learning and Accelerated Discovery; 1. Introduction; 1.1 Sample Use Case; 1.1.1 Description; 1.1.2 Current Research Process; 1.1.3 Problems with the Current Process; 1.1.4 The Future Process; 1.1.5 Benefits of the Future Process; 2. How Document Informatics Will Aid Materials Discovery; 2.1 Motivation; 2.2 Big Data Justification; 2.3 Challenges of Meta-Learning in Materials Research; 3. The General Research Framework
- 4. Pilot Implementation4.1 Objective 1: To Design and Develop a Time-Based, Hierarchical Topic Model; 4.1.1 Problem Description; 4.1.2 Prior Work; 4.1.3 Research Contributions; 4.2 Objective 2: To Implement Algorithms for Extracting Text from x-y Plots and Tables; 4.2.1 Problem Description; 4.2.2 Prior Work; 4.2.3 Research Contributions; 4.3 Objective 3: To Develop an Interactive, Materials Network Visualization Tool; 4.3.1 Problem Description; 4.3.2 Prior Work; 4.3.3 Research Contributions; 4.4 Testing and Validation; References
- Chapter 2: An Introduction to Rare Event Simulation and Importance Sampling1. Introduction: Monte Carlo Methods, Rare Event Simulation, and Variance Reduction Techniques; 2. MC Methods and the Problem of Rare Events; 2.1 MC Estimators; 2.2 The Problem of Rare Events; 3. Importance Sampling; 3.1 Importance-Sampled MC Estimators; 3.2 A Simple Example; 3.3 The Optimal Biasing Distribution; 3.4 Common Biasing Choices and Their Drawbacks; 4. Multiple IS; 4.1 Multiple IS: General Formulation; 4.2 The Balance Heuristics; 4.3 Application: Numerical Estimation of Probability Density Functions
- 5. The Cross-Entropy Method6. MCMC: Rejection Sampling, the Metropolis Method, and Gibbs Sampling; 7. Applications of VRTs to Error Estimation in Optical Fiber Communication Systems; 7.1 Polarization-Mode Dispersion; 7.2 Noise-Induced Perturbations; 8. Large Deviations Theory, Asymptotic Efficiency, and Final Remarks; References; Chapter 3: A Large-Scale Study of Language Usage as a Cognitive Biometric Trait; 1. Introduction; 2. Cognitive Fingerprints: Problem Description; 3. Data Description; 4. Methodology; 5. Results; 5.1 Evaluating Performance on Different Types of Data
- 5.2 Evaluating Performance of the Biometric Trait5.3 Impact of Features; 5.4 Using Authors with Different Minimum Number of Blogs; 5.5 Varying the Number of Blogs per Author; 5.6 Odd Man Out Analysis; 6. Discussions; 7. Related Work; 8. Conclusions and Future Work; Acknowledgment; References; Chapter 4: Customer Selection Utilizing Big Data Analytics; 1. Introduction; 1.1 Prior Work; 1.2 Goal; 2. Methodology; 2.1 Response Modeling; 2.2 Customer Selection; 2.2.1 Customer Selection Problem Setting; 2.2.2 The Optimization Problem Transformation; 2.2.3 Previous Approaches to Solve the SKP