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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https://doi.org/10.1007/978-3-319-99127-6In this chapter, we describe the specific characteristics of sensor data and sensor networks. Furthermore, we identify the most promising streaming models, which can be embedded in intelligent sensor platforms and used to mine real-time data for a variety of analytical insights.

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Sensor Data Surveillance,egy to continuously maintain . trend clusters across a sensor network. The analysis of trend clusters, which are discovered at the consecutive sliding windows, is useful to look for possible changes in the data, as well as to produce forecasts of the future.

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Sensor Networks and Data Streams: Basics,In this chapter, we describe the specific characteristics of sensor data and sensor networks. Furthermore, we identify the most promising streaming models, which can be embedded in intelligent sensor platforms and used to mine real-time data for a variety of analytical insights.

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Annalisa Appice,Anna Ciampi,Donato MalerbaIntroduces 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

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SpringerBriefs in Computer Sciencehttp://image.papertrans.cn/d/image/262910.jpg

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Data Mining Techniques in Sensor Networks978-1-4471-5454-9Series ISSN 2191-5768 Series E-ISSN 2191-5776

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https://doi.org/10.1007/978-3-319-99127-6 environmental phenomena that can be detected, monitored, and reacted to. Another important aspect is the real-time data delivery of novel platforms. In this chapter, we describe the specific characteristics of sensor data and sensor networks. Furthermore, we identify the most promising streaming mo