Similarity search over time-series data using wavelets
Popivanov, I.; Miller, R.J.
Data Engineering, 2002. Proceedings. 18th International Conference on
Volume , Issue , 2002 Page(s):212 - 221
Digital Object Identifier 10.1109/ICDE.2002.994711
Summary:Considers the use of wavelet transformations as a dimensionality
reduction technique to permit efficient similarity searching over
high-dimensional time-series data. While numerous transformations have
been proposed and studied, the only wavelet that has been shown to be
effective for this application is the Haar wavelet. In this work, we
observe that a large class of wavelet transformations (not only
orthonormal wavelets but also bi-orthonormal wavelets) can be used to
support similarity searching. This class includes the most popular and
most effective wavelets being used in image compression. We present a
detailed performance study of the effects of using different wavelets on
the performance of similarity searching for time-series data. We include
several wavelets that outperform both the Haar wavelet and the
best-known non-wavelet transformations for this application. To ensure
our results are usable by an application engineer, we also show how to
configure an indexing strategy for the best-performing transformations.
Finally, we identify classes of data that can be indexed efficiently
using these wavelet transformations
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