NDLI: Online event-driven subsequence matching over financial data streams

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Online event-driven subsequence matching over financial data streams

Content Provider	ACM Digital Library
Author	Wu, Huanmei Salzberg, Betty Zhang, Donghui
Abstract	Subsequence similarity matching in time series databases is an important research area for many applications. This paper presents a new approximate approach for automatic online subsequence similarity matching over massive data streams. With a simultaneous on-line segmentation and pruning algorithm over the incoming stream, the resulting piecewise linear representation of the data stream features high sensitivity and accuracy. The similarity definition is based on a permutation followed by a metric distance function, which provides the similarity search with flexibility, sensitivity and scalability. Also, the metric-based indexing methods can be applied for speed-up. To reduce the system burden, the event-driven similarity search is performed only when there is a potential event. The query sequence is the most recent subsequence of piecewise data representation of the incoming stream which is automatically generated by the system. The retrieved results can be analyzed in different ways according to the requirements of specific applications. This paper discusses an application for future data movement prediction based on statistical information. Experiments on real stock data are performed. The correctness of trend predictions is used to evaluate the performance of subsequence similarity matching.
Starting Page	23
Ending Page	34
Page Count	12
File Format	PDF
ISBN	1581138598
DOI	10.1145/1007568.1007574
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2004-06-13
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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