| 书目名称 | Data Mining Techniques in Sensor Networks | | 副标题 | Summarization, Inter | | 编辑 | Annalisa Appice,Anna Ciampi,Donato Malerba | | 视频video | | | 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 | | 丛书名称 | SpringerBriefs in Computer Science | | 图书封面 |  | | 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 | | 关键词 | Anomaly Detection; Clustering; Data Mining; Interpolation; Sensor Data; Spatio-Temporal Data Mining; Strea | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4471-5454-9 | | isbn_softcover | 978-1-4471-5453-2 | | isbn_ebook | 978-1-4471-5454-9Series ISSN 2191-5768 Series E-ISSN 2191-5776 | | issn_series | 2191-5768 | | copyright | The Author(s) 2014 |
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发表于 2025-3-21 16:41:02
