Veracity of Big Data
Machine Learning and Other Approaches to Verifying Truthfulness
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Beschreibung
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
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 Twitterhave 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
Software developers and practitioners, practicing engineers, curious managers, graduate students, and research scholars
Presents solutions to a problem that is intimidatingly complex, increasingly important, and largely unsolved Provides simple, easy-to-understand explanations of profound mathematical concepts Includes an appropriate mix of theory and practice to present practical and interesting approaches Opens the conversation on niche solutions that can play a significant role in the evolution of the research into big data veracity
Autor*in
Vishnu Pendyala
Themen in »Veracity of Big Data«
Big Data Machine Learning Veracity of Data Sentiment Analysis Kalman Filter Natural Language Understand Knowledge Representation Techniques Ensemble Methods