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Titlebook: Data Mining Techniques in Sensor Networks; Summarization, Inter Annalisa Appice,Anna Ciampi,Donato Malerba Book 2014 The Author(s) 2014 Ano

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書目名稱Data Mining Techniques in Sensor Networks
副標(biāo)題Summarization, Inter
編輯Annalisa Appice,Anna Ciampi,Donato Malerba
視頻videohttp://file.papertrans.cn/263/262910/262910.mp4
概述Introduces the trend cluster, a recently defined spatio-temporal pattern, and its use in summarizing, interpolating and identifying anomalies in sensor networks.Illustrates the application of trend cl
叢書名稱SpringerBriefs in Computer Science
圖書封面Titlebook: Data Mining Techniques in Sensor Networks; Summarization, Inter Annalisa Appice,Anna Ciampi,Donato Malerba Book 2014 The Author(s) 2014 Ano
描述Sensor networks comprise of a number of sensors installed across a spatially distributed network, which gather information and periodically feed a central server with the measured data. The server monitors the data, issues possible alarms and computes fast aggregates. As data analysis requests may concern both present and past data, the server is forced to store the entire stream. But the limited storage capacity of a server may reduce the amount of data stored on the disk. One solution is to compute summaries of the data as it arrives, and to use these summaries to interpolate the real data. This work introduces a recently defined spatio-temporal pattern, called trend cluster, to summarize, interpolate and identify anomalies in a sensor network. As an example, the application of trend cluster discovery to monitor the efficiency of photovoltaic power plants is discussed. The work closes with remarks on new possibilities for surveillance enabled by recent developments in sensing technology.
出版日期Book 2014
關(guān)鍵詞Anomaly Detection; Clustering; Data Mining; Interpolation; Sensor Data; Spatio-Temporal Data Mining; Strea
版次1
doihttps://doi.org/10.1007/978-1-4471-5454-9
isbn_softcover978-1-4471-5453-2
isbn_ebook978-1-4471-5454-9Series ISSN 2191-5768 Series E-ISSN 2191-5776
issn_series 2191-5768
copyrightThe Author(s) 2014
The information of publication is updating

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Sensor Data Surveillance,continuous surveillance of this unbounded amount of georeferenced data. Trend cluster discovery, as a spatiotemporal aggregate operator, may play a crucial role in the surveillance process of the sensor data. We describe a computation-preserving algorithm, which employs an incremental learning strat
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Book 2014twork. As an example, the application of trend cluster discovery to monitor the efficiency of photovoltaic power plants is discussed. The work closes with remarks on new possibilities for surveillance enabled by recent developments in sensing technology.
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Volker Stich,Gerhard Gudergan,Violett Zellerg the . approach, while the latter uses .. Both have been adapted to a sensor network scenario. The proposed techniques have been evaluated in a large air-climate sensor network. The empirical study compares the accuracy and efficiency of both techniques.
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Missing Sensor Data Interpolation,g the . approach, while the latter uses .. Both have been adapted to a sensor network scenario. The proposed techniques have been evaluated in a large air-climate sensor network. The empirical study compares the accuracy and efficiency of both techniques.
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