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: Crowdsourced Data Management
    sub title: Hybrid Machine-Human Computing
  name:
    name part: Li, Guoliang.
    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: Wang, Jiannan.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Zheng, Yudian.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Fan, Ju.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Franklin, Michael J.
    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: si
    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: XII, 159 p. 66 illus., 42 illus. in color. online resource.
  abstract: This book provides an overview of crowdsourced data management. Covering all aspects including the workflow, algorithms and research potential, it particularly focuses on the latest techniques and recent advances. The authors identify three key aspects in determining the performance of crowdsourced data management: quality control, cost control and latency control. By surveying and synthesizing a wide spectrum of studies on crowdsourced data management, the book outlines important factors that need to be considered to improve crowdsourced data management. It also introduces a practical crowdsourced-database-system design and presents a number of crowdsourced operators. Self-contained and covering theory, algorithms, techniques and applications, it is a valuable reference resource for researchers and students new to crowdsourced data management with a basic knowledge of data structures and databases.
  table of contents: 1. Introduction -- 2. Crowdsourcing Background. 3. Quality Control -- 4. Cost Control -- 5. Latency Control -- 6. Crowdsourcing Database Systems and Optimization -- 7. Crowdsourced Operators -- Conclusion.
  note: by Guoliang Li, Jiannan Wang, Yudian Zheng, Ju Fan, Michael J. Franklin.
  subject:
    topic: Big data
  subject:
    topic: Database management
  subject:
    topic: Special purpose computers
  subject:
    topic: Data mining
  subject:
    topic: Mobile computing
  subject:
    topic: Big Data
  subject:
    topic: Database Management
  subject:
    topic: Special Purpose and Application-Based Systems
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Mobile Computing
  classification: QA76.9.B45
  classification: 005.7
  related item:
    title info:
      title: Springer eBooks
  identifier: 9789811078477
  identifier: https://doi.org/10.1007/978-981-10-7847-7
  record info:
    record creation date: 181012
    record change date: 20191029001911.0
    record identifier: 978-981-10-7847-7