Examine the problem of maintaining the quality of big data and discover novel solutions. You will learn the four V’s of big data, including veracity, and study the problem from various angles. The solutions discussed are drawn from diverse areas of engineering and math, including machine learning, statistics, formal methods, and the Blockchain technology.
Veracity of Big Data serves as an introduction to machine learning algorithms and diverse techniques such as the Kalman filter, SPRT, CUSUM, fuzzy logic, and Blockchain, showing how they can be used to solve problems in the veracity domain. Using examples, the math behind the techniques is explained in easy-to-understand language.
Determining the truth of big data in real-world applications involves using various tools to analyze the available information. This book delves into some of the techniques that can be used. Microblogging websites such as Twitter have played a major role in public life, including during presidential elections. The book uses examples of microblogs posted on a particular topic to demonstrate how veracity can be examined and established. Some of the techniques are described in the context of detecting veiled attacks on microblogging websites to influence public opinion.
What You'll Learn
- Understand the problem concerning data veracity and its ramifications
- Develop the mathematical foundation needed to help minimize the impact of the problem using easy-to-understand language and examples
- Use diverse tools and techniques such as machine learning algorithms, Blockchain, and the Kalman filter to address veracity issues
Who This Book Is For
Software developers and practitioners, practicing engineers, curious managers, graduate students, and research scholars
| About the Author |
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ix | |
| Acknowledgments |
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| Introduction |
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| Chapter 1 The Big Data Phenomenon |
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| Chapter 2 Veracity of Web Information |
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Characteristics of a Trusted Website |
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| Chapter 3 Approaches to Establishing Veracity of Big Data |
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Natural Language Processing |
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Information Retrieval Techniques |
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| Chapter 4 Change Detection Techniques |
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Sequential Probability Ratio Test (SPRT) |
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| Chapter 5 Machine Learning Algorithms |
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The Microblogging Example |
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Collecting the Ground Truth |
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Artificial Neural Networks |
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| Chapter 6 Formal Methods |
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| Chapter 7 Medley of More Methods |
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151 | (3) |
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| Chapter 8 The Future: Blockchain and Beyond |
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158 | (9) |
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Blockchain for Big Data Veracity |
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| Summary |
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| Index |
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171 | |
Vishnu Pendyala is a Senior Member of IEEE and of the Computer Society of India (CSI), with over two decades of software experience with industry leaders such as Cisco, Synopsys, Informix (now IBM), and Electronics Corporation of India Limited. He is on the executive council of CSI, a member of the Special Interest Group on Big Data Analytics, and is the founding editor of its flagship publication, Visleshana. He recently taught a short-term course on Big Data Analytics for Humanitarian Causes, which was sponsored by the Ministry of Human Resources, Government of India under the GIAN scheme, and he delivered multiple keynote presentations at IEEE-sponsored international conferences. Vishnu has been living and working in the Silicon Valley for over two decades.
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