rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Veracity of Big Data
    sub title: Machine Learning and Other Approaches to Verifying Truthfulness
  name:
    name part: Pendyala, Vishnu.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2018
    edition: 1st ed. 2018.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIV, 180 p. 41 illus. online resource.
  abstract: 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.
  table of contents: 1 The Big Data Phenomenon -- 2 Veracity of Web Information -- 3 Approaches to Big Data Veracity -- 4 Change Detection Techniques -- 5 Machine Learning Algorithms -- 6 Formal Methods and Knowledge Representation -- 7 Medley of More Methods -- 8 The Future: Blockchain and Beyond.-.
  note: by Vishnu Pendyala.
  subject:
    topic: Big data
  subject:
    topic: Artificial intelligence
  subject:
    topic: Big Data
  subject:
    topic: Artificial Intelligence
  classification: QA76.9.B45
  classification: 005.7
  related item:
    title info:
      title: Springer eBooks
  identifier: 9781484236338
  identifier: https://doi.org/10.1007/978-1-4842-3633-8
  record info:
    record creation date: 180608
    record change date: 20191030082517.0
    record identifier: 978-1-4842-3633-8