| Description |
1 online resource (525 pages) |
|
text txt rdacontent |
|
computer c rdamedia |
|
online resource cr rdacarrier |
| Series |
Handbook of statistics ; volume 33 |
|
Handbook of statistics (Amsterdam, Netherlands) ; v. 33.
|
| Note |
Print version record. |
| Summary |
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 |
| Bibliography |
Includes bibliographical references and index. |
| Contents |
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 |
| Subject |
Big data -- Statistical methods.
|
|
Données volumineuses -- Méthodes statistiques.
|
|
COMPUTERS -- General.
|
| Added Author |
Govindaraju, Venugopal, editor.
|
|
Raghavan, Vijay, editor.
|
|
Rao, C. Radhakrishna (Calyampudi Radhakrishna), 1920-2023, editor.
|
| Other Form: |
Print version: Big data analytics 9780444634924 (OCoLC)914463722 |
| ISBN |
9780444634979 (electronic bk.) |
|
0444634975 (electronic bk.) |
|
9780444634924 |
|
0444634924 |
| Standard No. |
AU@ 000055231194 |
|
AU@ 000055409357 |
|
DKDLA 820120-katalog:9910110218705765 |
|
GBVCP 833092227 |
|
GBVCP 879385065 |
|
