Title :
Parallel Dimensionality Reduction Transformation for Time-Series Data
Author :
Thanh, Hoang Chi
Author_Institution :
Dept. of Inf., Hanoi Univ. of Sci., Hano, Vietnam
Abstract :
The subsequence matching in large time-series databases has been being an interesting problem. Many methods have been proposed that cope with this problem in an adequate extend. One of good ideas is reducing properly the dimensionality of time-series data. In this paper, we propose a method to reduce the dimensionality of high-dimensional time-series data. The method is simpler than existing ones based on the discrete Fourier transform and the discrete cosine transform. Furthermore, our dimensionality reduction may be executed in parallel. It preserves planar geometric blocks and may be applied to minimum bounding rectangles as well.
Keywords :
database theory; discrete Fourier transforms; discrete cosine transforms; time series; very large databases; dimensionality reduction; discrete Fourier transform; discrete cosine transform; large time-series databases; minimum bounding rectangles; parallel dimensionality reduction transformation; planar geometric blocks; time-series data; Database systems; Deductive databases; Discrete Fourier transforms; Discrete cosine transforms; Exchange rates; Humans; Informatics; Speech; Time-series data; dimensionality reduction; matching problem; minimum bounding rectangle;
Conference_Titel :
Intelligent Information and Database Systems, 2009. ACIIDS 2009. First Asian Conference on
Conference_Location :
Dong Hoi
Print_ISBN :
978-0-7695-3580-7
DOI :
10.1109/ACIIDS.2009.48