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Efficient Stream Sequence Matching Algorithms for Handheld Devices over Time-Series Stream Data  

Moon Yang-Sae (강원대학교 컴퓨터과학과)
Loh Woong-Kee (한국과학기술원 전산학과/첨단정보기술연구센터)
Publication Information
The Journal of Korean Institute of Communications and Information Sciences / v.31, no.8B, 2006 , pp. 736-744 More about this Journal
Abstract
For the handhold devices, minimizing repetitive CPU operations such as multiplications is a major factor for their performances. In this paper, we propose efficient algorithms for finding similar sequences from streaming time-series data such as stock prices, network traffic data, and sensor network data. First, we formally define the problem of similar subsequence matching from streaming time-series data, which is called the stream sequence matching in this paper. Second, based on the window construction mechanism adopted by the previous subsequence matching algorithms, we present an efficient window-based approach that minimizes CPU operations required for stream sequence matching. Third, we propose a notion of window MBR and present two stream sequence matching algorithms based on the notion. Fourth, we formally prove correctness of the proposed algorithms. Finally, through a series of analyses and experiments, we show that our algorithms significantly outperform the naive algorithm. We believe that our window-based algorithms are excellent choices for embedded stream sequence matching in handhold devices.
Keywords
Stream data; Time-series data; Stream sequence matching; Handhold devices;
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