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: Data Stream Management
    sub title: Processing High-Speed Data Streams
  name:
    name part: Garofalakis, Minos.
    role:
      role term: editor.
  name:
    name part: Gehrke, Johannes.
    role:
      role term: editor.
  name:
    name part: Rastogi, Rajeev.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2016
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: VII, 537 p. 103 illus., 16 illus. in color. online resource.
  abstract: We live in the era of “Big Data”: Petabytes of digital information are generated daily, and need to be processed and analyzed for interesting patterns and trends. Besides volume, a defining characteristic of Big Data is its velocity; that is, data is instantiated in the form of continuous, high-speed data streams that arrive at rapid rates, and need to be processed and analyzed on a continuous (24x7) basis. Such data streams pose very difficult challenges for conventional data-management architectures, which are built primarily on the concept of persistent, static data collections. This volume focuses on the theory and practice of data stream management, and the novel challenges this emerging domain poses for data-management algorithms, systems, and applications. The collection of chapters, contributed by authorities in the field, offers a comprehensive introduction to both the algorithmic/theoretical foundations of data streams, as well as the streaming systems and applications built in different domains. A short introductory chapter provides a brief summary of some basic data streaming concepts and models, and discusses the key elements of a generic stream query processing architecture. Subsequently, Part I focuses on basic streaming algorithms for some key analytics functions (e.g., quantiles, norms, join aggregates, heavy hitters) over streaming data. Part II then examines important techniques for basic stream mining tasks (e.g., clustering, classification, frequent itemsets). Part III discusses a number of advanced topics on stream processing algorithms, and Part IV focuses on system and language aspects of data stream processing with surveys of influential system prototypes and language designs. Part V then presents some representative applications of streaming techniques in different domains (e.g., network management, financial analytics). Finally, the volume concludes with an overview of current data streaming products and new application domains (e.g. cloud…
  table of contents: Part I: Introduction -- Part II: Computation of Basic Stream Synopses -- Part III: Mining Data Streams -- Part IV: Advanced Topics -- Part V: Systems and Architectures -- Part VI: Applications. .
  note: edited by Minos Garofalakis, Johannes Gehrke, Rajeev Rastogi.
  subject:
    topic: Computer science
  subject:
    topic: Big data
  subject:
    topic: Data structures (Computer science)
  subject:
    topic: Database management
  subject:
    topic: Data mining
  subject:
    topic: Information storage and retrieval
  subject:
    topic: Computer Science
  subject:
    topic: Database Management
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Big Data/Analytics
  subject:
    topic: Data Structures
  subject:
    topic: Information Storage and Retrieval
  classification: QA76.9.D3
  classification: 005.74
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Data-Centric Systems and Applications
  identifier: 9783540286080
  identifier: http://dx.doi.org/10.1007/978-3-540-28608-0
  record info:
    record creation date: 160711
    record change date: 20160719141619.0
    record identifier: 978-3-540-28608-0