Veracity of big data : machine learning and other approaches to verifying truthfulness
著者
書誌事項
Veracity of big data : machine learning and other approaches to verifying truthfulness
Apress, c2018
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注記
Includes index
内容説明・目次
内容説明
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
目次
Chapter 1: Introduction Chapter Goal: Introduce the readers to the manifestations of falsehood in Big Data and its ramifications.No of pages 30Sub -Topics1. The Big Data Phenomenon2. The Four V's3. Veracity - the fourth 'V'4. Tracing truth in human endeavors5. Veracity in the context of the WebChapter 2: Mathematical AbstractionChapter Goal: Present the math behind the method and develop a mathematical framework within which the problem and its solution can be discussed.No of pages: 30Sub - Topics 1. A fruit vendor example2. Building the abstraction3. Twitter Example - Sentiment Analysis4. Solution SpaceChapter 3: Tools and TechniquesChapter Goal: Introduce the Machine Learning and mathematical tools to solve the problem. No of pages : 30Sub - Topics: 1. Machine Learning Algorithms - a quick primer2. Kalman Filter3. Statistical Techniques
Chapter 4: Veracity of Web InformationChapter Goal: Use the concepts, tools, and techniques described in chapter 3 to examine the truthfulness of microblogsNo of pages: 50Sub - Topics: 1. Machine Learning the truthfulness of twitter data2. Statistical approaches to detect veiled attacks3. Applying Kalman Filter to analyze sentiment fluctuations
Chapter 5: Future DirectionsChapter Goal: Explore ideas that the readers can consider for further delving into the topic, given that this is a niche area.1. Natural Language Processing methods2. Knowledge Representation Techniques3. Ensemble Methods
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