Online event-driven subsequence matching over financial data streams

Huanmei Wu, Betty Salzberg, Donghui Zhang

Research output: Contribution to journalConference article

90 Scopus citations


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 online 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.

Original languageEnglish (US)
Pages (from-to)23-34
Number of pages12
JournalProceedings of the ACM SIGMOD International Conference on Management of Data
StatePublished - Jul 27 2004
Externally publishedYes
EventProceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2004 - Paris, France
Duration: Jun 13 2004Jun 18 2004

ASJC Scopus subject areas

  • Software
  • Information Systems

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