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 Mining Techniques in Sensor Networks
    sub title: Summarization, Interpolation and Surveillance
  name:
    name part: Appice, Annalisa.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Ciampi, Anna.
    role:
      role term: author.
  name:
    name part: Fumarola, Fabio.
    role:
      role term: author.
  name:
    name part: Malerba, Donato.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxk
    date issued: 2014
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIII, 105 p. 39 illus., 37 illus. in color. online resource.
  abstract: Emerging real life applications, such as environmental compliance, ecological studies and meteorology, are characterized by real-time data acquisition through a number of (wireless) remote sensors. Operatively, remote sensors are installed across a spatially distributed network; they gather information along a number of attribute dimensions and periodically feed a central server with the measured data. The server is required to monitor these data, issue possible alarms or compute fast aggregates. As data analysis requests, which are submitted to a server, may concern both present and past data, the server is forced to store the entire stream. But, in the case of massive streams (large networks and/or frequent transmissions), the limited storage capacity of a server may impose to reduce the amount of data stored on the disk. One solution to address the storage limits is to compute summaries of the data as they arrive and use these summaries to interpolate the real data which are discarded instead. On any future demands of further analysis of the discarded data, the server pieces together the data from the summaries stored in database and processes them according to the requests. This work introduces the multiple possibilities and facets of a recently defined spatio-temporal pattern, called trend cluster, and its applications to summarize, interpolate and identify anomalies in a sensor network. As an example application, the authors illustrate the application of trend cluster discovery to monitor the efficiency of photovoltaic power plants. The work closes with remarks on new possibilities for surveillance gained by recent developments of sensing technology, and with an outline of future challenges.
  table of contents: Introduction -- Sensor Networks and Data Streams: Basics -- Geodata Stream Summarization -- Missing Sensor Data Interpolation -- Sensor Data Surveillance -- Sensor Data Analysis Applications.
  note: by Annalisa Appice, Anna Ciampi, Fabio Fumarola, Donato Malerba.
  subject:
    topic: Computer science
  subject:
    topic: Computer communication systems
  subject:
    topic: Data mining
  subject:
    topic: Computer Science
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Computer Communication Networks
  classification: QA76.9.D343
  classification: 006.312
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: SpringerBriefs in Computer Science
  identifier: 9781447154549
  identifier: http://dx.doi.org/10.1007/978-1-4471-5454-9
  record info:
    record creation date: 130912
    record change date: 20151030071419.0
    record identifier: 978-1-4471-5454-9