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Published Articles >> Table of Contents >> Abstract
18th International Conference on Scientific and Statistical Database Management (SSDBM'06)
pp. 241-250
Efficient Methods on Predictions for Similarity Search over Stream Time Series
Xiang Lian, Hong Kong University of Science and Technology
Lei Chen, Hong Kong University of Science and Technology
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/SSDBM.2006.22
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| Abstract |
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Due to the wide usage of stream time series, an
efficient and effective similarity search over stream data
becomes essential for many applications. Although
many approaches have been proposed for searching
through archived data, because of the unique
characteristics of the stream, for example, data are
frequently updated, traditional methods may not work
for the stream time series. Especially, for the cases
where the arrival of data is often delayed for various
reasons, for example, the communication congestion or
batch processing and so on, queries on such incomplete
time series or even future time series may result in
inaccuracy. Therefore, in this paper we propose two
approaches, polynomial and probabilistic, to predict the
unknown values that have not arrived at the system. We
also present efficient indexes, that is, a
multidimensional hash index and B+-tree, to facilitate
the prediction and similarity search on future time
series, respectively. Extensive experiments demonstrate
the efficiency and effectiveness of our methods in terms
of I/O, prediction and query accuracy.
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 Additional Information
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Citation:
Xiang Lian, Lei Chen,
"Efficient Methods on Predictions for Similarity Search over Stream Time Series,"
ssdbm,
pp. 241-250,
18th International Conference on Scientific and Statistical Database Management (SSDBM'06),
2006
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