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|a 1484236327
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|a 9781484236338
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|a 1484236335
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|a QA76.9.A43
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|a Pendyala, Vishnu
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|a Veracity of big data
|b machine learning and other approaches to verifying truthfulness
|c Vishnu Pendyala
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|a [United States]
|b Apress
|c 2018
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| 300 |
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|a xiv, 180 pages
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|a Sequential Probability Ratio Test (SPRT)The CUSUM Technique; Kalman Filter; Summary; Chapter 5: Machine Learning Algorithms; The Microblogging Example; Collecting the Ground Truth; Logistic Regression; Naïve Bayes Classifier; Support Vector Machine; Artificial Neural Networks; K-Means Clustering; Summary; Chapter 6: Formal Methods; Terminology; Propositional Logic; Predicate Calculus; Fuzzy Logic; Summary; Chapter 7: Medley of More Methods; Collaborative Filtering; Vector Space Model; Summary; Chapter 8: The Future: Blockchain and Beyond; Blockchain Explained; Blockchain for Big Data Veracity
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|a Includes bibliographical references and index
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|a Intro; Table of Contents; About the Author; Acknowledgments; Introduction; Chapter 1: The Big Data Phenomenon; Why "Big" Data; The V's of Big Data; Veracity -- The Fourth 'V'; Summary; Chapter 2: Veracity of Web Information; The Problem; The Causes; The Effects; The Remedies; Characteristics of a Trusted Website; Summary; Chapter 3: Approaches to Establishing Veracity of Big Data; Machine Learning; Change Detection; Optimization Techniques; Natural Language Processing; Formal Methods; Fuzzy Logic; Information Retrieval Techniques; Blockchain; Summary; Chapter 4: Change Detection Techniques
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|a Data editing / fast
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|a Computer algorithms / http://id.loc.gov/authorities/subjects/sh91000149
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|a Databases / Evaluation
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|a Intégrité des données
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|a Verification (Logic) / Computer programs
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|a Data integrity / fast
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|a Computer algorithms / fast
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|a Artificial intelligence / bicssc
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|a Algorithms
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|a Data editing / http://id.loc.gov/authorities/subjects/sh85035855
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|a Algorithmes
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|a algorithms / aat
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|a Vérification (Logique) / Logiciels
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|a Data integrity / http://id.loc.gov/authorities/subjects/sh2008003747
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|a COMPUTERS / General / bisacsh
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|a Databases / bicssc
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|a Databases / Evaluation / fast
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|a Édition (Informatique)
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|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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|a 10.1007/978-1-4842-3633-8
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|a GBB8O3826
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| 776 |
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|z 9781484236338
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| 776 |
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|z 1484236335
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| 776 |
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|z 1484236327
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|z 9781484236321
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|u https://learning.oreilly.com/library/view/~/9781484236338/?ar
|x Verlag
|3 Volltext
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|a 005.1
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| 520 |
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|a 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
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