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Multiscale Representations for Fast Pattern Matching in Stream Time Series
April 2009 (vol. 21 no. 4)
pp. 568-581
ASCII Text
x 
 
Xiang Lian, Lei Chen, Jeffrey Xu Yu, Jinsong Han, Jian Ma, "Multiscale Representations for Fast Pattern Matching in Stream Time Series," IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 4, pp. 568-581, April, 2009.
 
 
BibTex
x
 
@article{ 10.1109/TKDE.2008.184,
author = {Xiang Lian and Lei Chen and Jeffrey Xu Yu and Jinsong Han and Jian Ma},
title = {Multiscale Representations for Fast Pattern Matching in Stream Time Series},
journal ={IEEE Transactions on Knowledge and Data Engineering},
volume = {21},
number = {4},
issn = {1041-4347},
year = {2009},
pages = {568-581},
doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2008.184},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Knowledge and Data Engineering
TI - Multiscale Representations for Fast Pattern Matching in Stream Time Series
IS - 4
SN - 1041-4347
SP568
EP581
EPD - 568-581
A1 - Xiang Lian,
A1 - Lei Chen,
A1 - Jeffrey Xu Yu,
A1 - Jinsong Han,
A1 - Jian Ma,
PY - 2009
KW - Information Storage and Retrieval
KW - Temporal databases
VL - 21
JA - IEEE Transactions on Knowledge and Data Engineering
ER -
 
Xiang Lian, Hong Kong University of Science and Technology, Hong Kong
Lei Chen, Hong Kong University of Science and Technology, Hong Kong
Jeffrey Xu Yu, The Chinese University of Hong Kong, Hong Kong
Jinsong Han, Hong Kong University of Science and Technology, Hong Kong
Jian Ma, Nokia Research Center, Beijing
Web Extra: View Supplemental Material
Similarity-based time series retrieval has been a subject of long term study due to its wide usage in many applications, such as financial data analysis and weather data forecasting. Its original task was to find those time series similar to a pattern time series data, where both the pattern and data time series are static. Recently, with an increasing demand on stream data management, similarity-based stream time series retrieval has raised new research issues due to its unique requirements during the stream processing, such as one-pass search and fast response. In this paper, we address the problem of matching both static and dynamic patterns over stream time series data. We will develop a novel multi-scale representation, called multi-scale segment mean (MSM), for stream time series data, which can be incrementally computed and thus perfectly adapted to the stream characteristics. Most importantly, we propose a novel multi-step filtering mechanism, SS, over the multi-scale representation. Analysis indicates that the mechanism can greatly prune the search space and thus offer fast response. Furthermore, batching processing optimization, the dynamic case where patterns are also from stream time series, and pattern matching over future stream time series are also discussed. Extensive experiments show the proposed scheme can efficiently filter out false candidates and detect patterns.

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Index Terms:
Information Storage and Retrieval, Temporal databases
Citation:
Xiang Lian, Lei Chen, Jeffrey Xu Yu, Jinsong Han, Jian Ma, "Multiscale Representations for Fast Pattern Matching in Stream Time Series," IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 4, pp. 568-581, April 2009, doi:10.1109/TKDE.2008.184
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