Veracity of Big Data: Machine Learning and Other Approaches to Verifying Truthfulness

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Veracity of Big Data: Machine Learning and Other Approaches to Verifying Truthfulness Front Cover

Veracity of Big Data: Machine Learning and Other Approaches to Verifying Truthfulness

by Vishnu Pendyala
  • Length: 180 pages
  • Edition: 1st ed.
  • Publisher: Apress
  • Publication Date: 2018-07-31
  • ISBN-10: 1484236327
  • ISBN-13: 9781484236321
  • Sales Rank: #5531693 (See Top 100 Books)
Description

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

Table of Contents

Chapter 1: The Big Data Phenomenon
Chapter 2: Veracity of Web Information
Chapter 3: Approaches to Establishing Veracity of Big Data
Chapter 4: Change Detection Techniques
Chapter 5: Machine Learning Algorithms
Chapter 6: Formal Methods
Chapter 7: Medley of More Methods
Chapter 8: The Future: Blockchain and Beyond

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