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: Knowledge Graphs and Big Data Processing
  name:
    name part: Janev, Valentina.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Graux, Damien.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Jabeen, Hajira.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: Sallinger, Emanuel.
    role:
      role term: editor.
    role:
      role term: edt
    role:
      role term: http://id.loc.gov/vocabulary/relators/edt
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  genre: Electronic books.
  origin info:
    place:
      place term: sz
    date issued: 2020
    edition: 1st ed. 2020.
    issuance: monographic
  language:
    language term: eng
  physical description:
    reformatting quality: access
    form: remote
    extent: 1 online resource (XI, 209 pages 39 illustrations, 32 illustrations in color.)
  abstract: This open access book is part of the LAMBDA Project (Learning, Applying, Multiplying Big Data Analytics), funded by the European Union, GA No. 809965. Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companies to make better decisions as well as to verify or disprove existing theories or models. The term data analytics is often used interchangeably with intelligence, statistics, reasoning, data mining, knowledge discovery, and others. The goal of this book is to introduce some of the definitions, methods, tools, frameworks, and solutions for big data processing, starting from the process of information extraction and knowledge representation, via knowledge processing and analytics to visualization, sense-making, and practical applications. Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions. This book is addressed to graduate students from technical disciplines, to professional audiences following continuous education short courses, and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required.
  table of contents: Foundations -- Chapter 1. Ecosystem of Big Data -- Chapter 2. Knowledge Graphs: The Layered Perspective -- Chapter 3. Big Data Outlook, Tools, and Architectures -- Architecture -- Chapter 4. Creation of Knowledge Graphs -- Chapter 5. Federated Query Processing -- Chapter 6. Reasoning in Knowledge Graphs: An Embeddings Spotlight -- Methods and Solutions -- Chapter 7. Scalable Knowledge Graph Processing using SANSA -- Chapter 8. Context-Based Entity Matching for Big Data -- Applications -- Chapter 9. Survey on Big Data Applications -- Chapter 10. Case Study from the Energy Domain.
  note: edited by Valentina Janev, Damien Graux, Hajira Jabeen, Emanuel Sallinger.
  note: Open Access
  subject:
    topic: Database management
  subject:
    topic: Application software
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computer logic
  subject:
    topic: Management information systems
  subject:
    topic: Database Management
  subject:
    topic: Information Systems Applications (incl. Internet)
  subject:
    topic: Logic in AI
  subject:
    topic: Computer Appl. in Administrative Data Processing
  subject:
    topic: Business Information Systems
  classification: QA76.9.D3
  classification: 005.74
  related item:
    title info:
      title: Springer Nature eBook
  related item:
    title info:
      title: Information Systems and Applications, incl. Internet/Web, and HCI ; 12072
  identifier: 9783030531997
  identifier: https://doi.org/10.1007/978-3-030-53199-7
  access condition: Open Access
  record info:
    record creation date: 200715
    record change date: 20230829020236.4
    record identifier: in00004381561