DocumentCode :
3107076
Title :
Window-based Tensor Analysis on High-dimensional and Multi-aspect Streams
Author :
Sun, Jimeng ; Papadimitriou, Spiros ; Yu, Philip S.
Author_Institution :
Carnegie Mellon Univ., Pittsburgh, PA
fYear :
2006
fDate :
18-22 Dec. 2006
Firstpage :
1076
Lastpage :
1080
Abstract :
Data stream values are often associated with multiple aspects. For example, each value from environmental sensors may have an associated type (e.g., temperature, humidity, etc) as well as location. Aside from timestamp, type and location are the two additional aspects. How to model such streams? How to simultaneously find patterns within and across the multiple aspects? How to do it incrementally in a streaming fashion? In this paper, all these problems are addressed through a general data model, tensor streams, and an effective algorithmic framework, window-based tensor analysis (WTA). Two variations of WTA, independent- window tensor analysis (IW) and moving-window tensor analysis (MW), are presented and evaluated extensively on real datasets. Finally, we illustrate one important application, multi-aspect correlation analysis (MACA), which uses WTA and we demonstrate its effectiveness on an environmental monitoring application.
Keywords :
data mining; environmental science computing; environmental monitoring application; high-dimensional streams; multi-aspect correlation analysis; multi-aspect streams; window-based tensor analysis; Algorithm design and analysis; Computational efficiency; Data mining; Data models; Humidity; Monitoring; Pattern analysis; Temperature sensors; Tensile stress;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining, 2006. ICDM '06. Sixth International Conference on
Conference_Location :
Hong Kong
ISSN :
1550-4786
Print_ISBN :
0-7695-2701-7
Type :
conf
DOI :
10.1109/ICDM.2006.169
Filename :
4053156
Link To Document :
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