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: Demystifying graph data science
    sub title: graph algorithms, analytics methods, platforms, databases, and use cases
  name:
    name part: Raj, Pethuru
    role:
      role term: editor.
  name:
    name part: Kumar, Abhishek
    role:
      role term: editor.
  name:
    name part: García Díaz, Vicente
    role:
      role term: editor.
  name:
    name part: Muthurama, Nachamai
    role:
      role term: editor.
  type of resource: text
  genre: elektronické knihy
  genre: electronic books
  origin info:
    place:
      place term: enk
    date issued: 2022
    copyright date: 2022
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (xxi, 391 pages) : illustrations.
  abstract: With the growing maturity and stability of digitization and edge technologies, vast numbers of digital entities, connected devices, and microservices interact purposefully to create huge sets of poly-structured digital data. Corporations are continuously seeking fresh ways to use their data to drive business innovations and disruptions to bring in real digital transformation. Data science (DS) is proving to be the one-stop solution for simplifying the process of knowledge discovery and dissemination out of massive amounts of multi-structured data. Supported by query languages, databases, algorithms, platforms, analytics methods and machine and deep learning (ML and DL) algorithms, graphs are now emerging as a new data structure for optimally representing a variety of data and their intimate relationships. Compared to traditional analytics methods, the connectedness of data points in graph analytics facilitates the identification of clusters of related data points based on levels of influence, association, interaction frequency and probability. Graph analytics is being empowered through a host of path-breaking analytics techniques to explore and pinpoint beneficial relationships between different entities such as organizations, people and transactions. This edited book aims to explain the various aspects and importance of graph data science. The authors from both academia and industry cover algorithms, analytics methods, platforms and databases that are intrinsically capable of creating business value by intelligently leveraging connected data. This book will be a valuable reference for ICTs industry and academic researchers, scientists and engineers, and lecturers and advanced students in the fields of data analytics, data science, cloud/fog/edge architecture, internet of things, artificial intelligence/machine and deep learning, and related fields of applications. It will also be of interest to analytics professionals in industry and IT operations teams.
  note: edited by Pethuru Raj, Abhishek Kumar, Vicente García Díaz and Nachamai Muthurama.
  note: Includes index.
  note: Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty
  subject:
    topic: Graph theory
    topic: Computer programs
  related item:
    identifier: (OCoLC)1322045952
  related item:
    title info:
      title: IET computing series ; 48
  identifier: 1839534893
  identifier: 9781839534898
  identifier: 1839534885
  identifier: 9781839534881
  identifier: https://proxy.k.utb.cz/login
  access condition: Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty
  record info:
    record content source: YDX
    record creation date: 220922
    record change date: 20240717213016.0
    record identifier: kn-on1345273842
    language of cataloging:
      language term: eng