Veracity of Big Data (eBook)
XIV, 180 Seiten
Apress (Verlag)
978-1-4842-3633-8 (ISBN)
- 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 LearnUnderstand the problem concerning data veracity and its ramificationsDevelop the mathematical foundation needed to help minimize the impact of the problem using easy-to-understand language and examplesUse diverse tools and techniques such as machine learning algorithms, Blockchain, and the Kalman filter to address veracity issuesWho This Book Is ForSoftware developers and practitioners, practicing engineers, curious managers, graduate students, and research scholars
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.
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 TechniquesChapter 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 fluctuationsChapter 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
| Erscheint lt. Verlag | 8.6.2018 |
|---|---|
| Zusatzinfo | XIV, 180 p. 41 illus. |
| Verlagsort | Berkeley |
| Sprache | englisch |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Mathematik / Informatik ► Informatik ► Netzwerke | |
| Mathematik / Informatik ► Informatik ► Software Entwicklung | |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | Big Data • Ensemble methods • Kalman Filter • Knowledge Representation Techniques • machine learning • Natural Language Understand • sentiment analysis • Veracity of Data |
| ISBN-10 | 1-4842-3633-5 / 1484236335 |
| ISBN-13 | 978-1-4842-3633-8 / 9781484236338 |
| Haben Sie eine Frage zum Produkt? |
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