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Data Mining Techniques in Sensor NetworksGeodata Stream Summarization

Data Mining Techniques in Sensor Networks: Geodata Stream Summarization [The management of massive amounts of geodata collected by sensor networks creates several challenges, including the real-time application of summarization techniques, which should allow the storage of this unbounded volume of georeferenced and timestamped data in a server with a limited memory for any future query. SUMATRA is a summarization technique, which accounts for spatial and temporal information of sensor data to produce the appropriate trade-off between size and accuracy of geodata summarization. It uses the count-based model to process the stream. In particular, it segments the stream into windows, computes summaries window-by-window, and stores these summaries in a database. The trend clusters are discovered as a summary of each window. They are clusters of georeferenced data, which vary according to a similar trend along the time horizon of the window. Signal compression techniques are also considered to derive a compact representation of these trends for storage in the database. The empirical analysis of trend clusters contributes to assess the summarization capability, the accuracy, and the efficiency of the trend cluster-based summarization schema in real applications. Finally, a stream cube, called geo-trend stream cube, is defined. It uses trends to aggregate a numeric measure, which is streamed by a sensor network and is organized around space and time dimensions. Space-time roll-up and drill-down operators allow the exploration of trends from a coarse-grained and inner-grained hierarchical view.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Data Mining Techniques in Sensor NetworksGeodata Stream Summarization

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References (28)

Publisher
Springer London
Copyright
© The Author(s) 2014
ISBN
978-1-4471-5453-2
Pages
9 –48
DOI
10.1007/978-1-4471-5454-9_2
Publisher site
See Chapter on Publisher Site

Abstract

[The management of massive amounts of geodata collected by sensor networks creates several challenges, including the real-time application of summarization techniques, which should allow the storage of this unbounded volume of georeferenced and timestamped data in a server with a limited memory for any future query. SUMATRA is a summarization technique, which accounts for spatial and temporal information of sensor data to produce the appropriate trade-off between size and accuracy of geodata summarization. It uses the count-based model to process the stream. In particular, it segments the stream into windows, computes summaries window-by-window, and stores these summaries in a database. The trend clusters are discovered as a summary of each window. They are clusters of georeferenced data, which vary according to a similar trend along the time horizon of the window. Signal compression techniques are also considered to derive a compact representation of these trends for storage in the database. The empirical analysis of trend clusters contributes to assess the summarization capability, the accuracy, and the efficiency of the trend cluster-based summarization schema in real applications. Finally, a stream cube, called geo-trend stream cube, is defined. It uses trends to aggregate a numeric measure, which is streamed by a sensor network and is organized around space and time dimensions. Space-time roll-up and drill-down operators allow the exploration of trends from a coarse-grained and inner-grained hierarchical view.]

Published: Sep 13, 2013

Keywords: Clustering Trend; Stream Cube; Signal Compression Techniques; Count-based Models; Cube Slice

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