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Data mining techniques in sensor networks [electronic resource] : summarization, interpolation and surveillance

Data mining techniques in sensor networks [electronic resource] : summarization, interpolation and surveillance

자료유형
E-Book(소장)
개인저자
Appice, Annalisa.
서명 / 저자사항
Data mining techniques in sensor networks [electronic resource] : summarization, interpolation and surveillance / Annalisa Appice ... [et al.].
발행사항
London :   Springer London :   Imprint: Springer,   2014.  
형태사항
1 online resource (xiii, 105 p.) : ill. (some col.).
총서사항
SpringerBriefs in computer science,2191-5768
ISBN
9781447154549
요약
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.
일반주기
Title from e-Book title page.  
내용주기
Introduction -- Sensor Networks and Data Streams: Basics -- Geodata Stream Summarization -- Missing Sensor Data Interpolation -- Sensor Data Surveillance -- Sensor Data Analysis Applications.
서지주기
Includes bibliographical references and index.
이용가능한 다른형태자료
Issued also as a book.  
일반주제명
Data mining. Sensor networks.
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245 0 0 ▼a Data mining techniques in sensor networks ▼h [electronic resource] : ▼b summarization, interpolation and surveillance / ▼c Annalisa Appice ... [et al.].
260 ▼a London : ▼b Springer London : ▼b Imprint: Springer, ▼c 2014.
300 ▼a 1 online resource (xiii, 105 p.) : ▼b ill. (some col.).
490 1 ▼a SpringerBriefs in computer science, ▼x 2191-5768
500 ▼a Title from e-Book title page.
504 ▼a Includes bibliographical references and index.
505 0 ▼a Introduction -- Sensor Networks and Data Streams: Basics -- Geodata Stream Summarization -- Missing Sensor Data Interpolation -- Sensor Data Surveillance -- Sensor Data Analysis Applications.
520 ▼a 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.
530 ▼a Issued also as a book.
538 ▼a Mode of access: World Wide Web.
650 0 ▼a Data mining.
650 0 ▼a Sensor networks.
700 1 ▼a Appice, Annalisa.
830 0 ▼a SpringerBriefs in computer science.
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945 ▼a KLPA
991 ▼a E-Book(소장)

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