DocumentCode :
2209361
Title :
Trend cluster based compression of geographically distributed data streams
Author :
Ciampi, Anna ; Appice, Annalisa ; Malerba, Donato ; Guccione, Pietro
Author_Institution :
Dipt. di Inf., Univ. Aldo Moro di Bari, Bari, Italy
fYear :
2011
fDate :
11-15 April 2011
Firstpage :
168
Lastpage :
175
Abstract :
In many real-time applications, such as wireless sensor network monitoring, traffic control or health monitoring systems, it is required to analyze continuous and unbounded geographically distributed streams of data (e.g. temperature or humidity measurements transmitted by sensors of weather stations). Storing and querying geo-referenced stream data poses specific challenges both in time (real-time processing) and in space (limited storage capacity). Summarization algorithms can be used to reduce the amount of data to be permanently stored into a data warehouse without losing information for further subsequent analysis. In this paper we present a framework in which data streams are seen as time-varying realizations of stochastic processes. Signal compression techniques, based on transformed domains, are applied and compared with a geometrical segmentation in terms of compression efficiency and accuracy in the subsequent reconstruction.
Keywords :
data analysis; data compression; data mining; data warehouses; stochastic processes; compression efficiency; continuous data streams; data analysis; data mining; data querying; data storage; data warehouse; geometrical segmentation; georeferenced stream data; health monitoring system; limited storage capacity; real-time processing; signal compression technique; stochastic process; summarization algorithm; time-varying realization; traffic control; trend cluster based compression; unbounded geographically distributed data streams; wireless sensor network monitoring; Approximation methods; Clustering algorithms; Data warehouses; Discrete Fourier transforms; Indexes; Prototypes; Sensors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Data Mining (CIDM), 2011 IEEE Symposium on
Conference_Location :
Paris
Print_ISBN :
978-1-4244-9926-7
Type :
conf
DOI :
10.1109/CIDM.2011.5949298
Filename :
5949298
Link To Document :
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