Online Event-driven Subsequence Matching over Financial Data Streams

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...

• Type: Conference
• Year: 2004
• Where: SIGMOD
• Authors: Huanmei Wu, Betty Salzberg, Donghui Zhang
• Keywords: Database, Event-driven Similarity Search, Online Subsequence Similarity, SIGMOD 2004, Subsequence Similarity