Veracity of Big Data

Machine Learning and Other Approaches to Verifying Truthfulness

verfasst von: Vishnu Pendyala

Verlag: Apress

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

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 LearnUnderstand 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

Inhaltsverzeichnis

Frontmatter

Chapter 1. The Big Data Phenomenon

Abstract

Data is fast becoming the most precious aspect of our lives. Today, we tend to guard our data much more securely than even money. Data in a number of forms is getting generated rapidly and in huge proportions, often not going through the much needed quality checks. This chapter examines some of the aspects of the vortex that this "Big Data" has created and touches upon the veracity related problems that resulted from it.

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Chapter 2. Veracity of Web Information

Abstract

There is increased focus on misinformation on the Web lately, particularly in the political circles. The recent testimony of Facebook's Mark Zuckerberg to the US Congress provides insights into the powerful role of Social Media in today's world. Web information, including that from the Social Media comprises a large chunk of the Big Data domain. This chapter tries to convey the magnitude of the problem that the lack of veracity of Big Data poses today, using the example of Web Information.

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Chapter 3. Approaches to Establishing Veracity of Big Data

Abstract

In the previous two chapters, we examined the role of Big Data and its veracity in today's world. We concluded that the problem of lack of veracity is significant and serious. In this chapter, we venture into the solution space. The chapter presents a brief overview of a number of technqiues from diverse aspects of computing that can possibly help with improving the veracity of Big Data.

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Chapter 4. Change Detection Techniques

Abstract

Change Detection is a commonly used technique in diverse applications such as machine vision, speech processing, and remote sensing. This chapter explains how Change Detection techniques can be used for verifying the truthfulness of information. We continue to use the microblogging example to explain the concepts presented in this chapter too.

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Chapter 5. Machine Learning Algorithms

Abstract

Learning is fundamental to our evolution, enrichment, and very existence. Over the years, people, particularly professionals like the law enforcement officers, lawyers, and judges have learned to distinguish truth from lies. In this chapter, we shall see how we can make machines to learn this important skill of determining the truthfulness of Big Data.

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Chapter 6. Formal Methods

Abstract

Truth is often a matter of logic and reasoning. Discussion on veracity is not complete without exploring how logic can help. In this chapter we consider modeling data in ways that formal methods such as Boolean logic can be used to solve veracity related issues.

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Chapter 7. Medley of More Methods

Abstract

As the saying goes, birds of the same feather flock together. Truth and lies can be grouped or distinguished using similarity measures as we shall see in this chapter. The techniques examined in this chapter are not primarily meant to solve veracity issues, but their application can be extended to the veracity domain as well, as detailed in this chapter.

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Chapter 8. The Future: Blockchain and Beyond

Abstract

Best solutions to a problem are the ones that address the root cause. Blockchain is one such solution to the veracity problem of Big Data. It provides a secure mechanism to track data. Using unique constructs, Blockchain makes sure that the data is tamper-proof. This chapter examines some of the concepts involved in Blockchain, that make it a promising technology that has the potential to make not only the data, but the future, more trustworthy.

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Backmatter

Titel: Veracity of Big Data
verfasst von: Vishnu Pendyala
Verlag: Apress
Electronic ISBN: 978-1-4842-3633-8
Print ISBN: 978-1-4842-3632-1
DOI: https://doi.org/10.1007/978-1-4842-3633-8