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Author Pendyala, Vishnu.

Title Veracity of big data : machine learning and other approaches to verifying truthfulness / Vishnu Pendyala.

Publication Info. [United States] : Apress, [2018]
2018

Copies

Location Call No. OPAC Message Status
 Axe Books 24x7 IT E-Book  Electronic Book    ---  Available
Description 1 online resource (xiv, 180 pages)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
text file PDF rda
Bibliography Includes bibliographical references and index.
Note Online resource; title from PDF title page (EBSCO, viewed June 13, 2018).
Contents 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.
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; Nave 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.
Summary 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.
Subject Verification (Logic) -- Computer programs.
Computer algorithms.
Databases -- Evaluation.
Data editing.
Data integrity.
COMPUTERS -- General.
Computer algorithms. (OCoLC)fst00872010
Data editing. (OCoLC)fst00887933
Data integrity. (OCoLC)fst01746571
Databases -- Evaluation. (OCoLC)fst00888069
Computer Science.
Big Data.
Computing Methodologies.
Artificial intelligence.
Databases.
Genre/Form Electronic books.
Electronic books.
Other Form: Print version: Pendyala, Vishnu. Veracity of big data. [United States] : Apress, [2018] 1484236327 9781484236321 (OCoLC)1028953663
ISBN 9781484236338 (electronic bk.)
1484236335 (electronic bk.)
1484236327
9781484236321
9781484236321
1484236327
Standard No. 10.1007/978-1-4842-3633-8 doi
9781484236321
GBVCP 1029873089
UKMGB 019183857

 
    
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