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Time series data is ubiquitous and plays an important role in virtually every domain. For example, in medicine, the advancement of computer technology has enabled more sophisticated patients monitoring, either on-site or remotely. Such monitoring produces massive amount of time series data, which contain valuable information for pattern learning and knowledge discovery. In this paper, we explore the problem of identifying frequently occurring patterns, or motifs, in streaming medical data. Thedoi:10.1109/cbms.2010.6042675 dblp:conf/cbms/LinL10 fatcat:cjbg6iw2fnaxplzrdekrqxe37m