DocumentCode :
860662
Title :
Multiscale Representations for Fast Pattern Matching in Stream Time Series
Author :
Lian, Xiang ; Chen, Lei ; Yu, Jeffrey Xu ; Han, Jinsong ; Ma, Jian
Author_Institution :
Dept. of Comput. Sci. & Eng., Hong Kong Univ. of Sci. & Technol., Kowloon
Volume :
21
Issue :
4
fYear :
2009
fDate :
4/1/2009 12:00:00 AM
Firstpage :
568
Lastpage :
581
Abstract :
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, weather data forecasting, and multimedia data retrieval. Its original task was to find those time series similar to a pattern (query) 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 multiscale representation, called multiscale segment mean, for stream time-series data, which can be incrementally computed and thus perfectly adapted to the stream characteristics. Most importantly, we propose a novel multistep filtering mechanism, step by step, over the multiscale representation. Analysis indicates that the mechanism can greatly prune the search space and thus offer fast response. Furthermore, batch processing optimization and the dynamic case where patterns are also from stream time series are discussed. Extensive experiments show the multiscale representation together with the multistep filtering scheme can efficiently filter out false candidates and detect patterns, compared to the multiscale wavelet.
Keywords :
batch processing (computers); information retrieval; pattern matching; time series; batch processing optimization; fast pattern matching; multiscale segment mean; multistep filtering scheme; pattern time-series data; similarity-based time-series retrieval; stream data management; stream time series; stream time-series retrieval; Data analysis; Information filtering; Information filters; Information retrieval; Monitoring; Pattern matching; Storms; Streaming media; Temperature sensors; Weather forecasting; Information Storage and Retrieval; Temporal databases;
fLanguage :
English
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1041-4347
Type :
jour
DOI :
10.1109/TKDE.2008.184
Filename :
4624258
Link To Document :
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