Much of the world's data is in the form of time series, and many other types of data, such as video, image, and handwriting, can easily be transformed into time series. This fact has fueled enormous interest in time series retrieval in the database and data mining community. However, much of this work's narrow focus on efficiency and scalability has come at the cost of usability and effectiveness. Here, we introduce a general framework that learns a distance measure with arbitrary constraints

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... the warping path of the Dynamic Time Warping calculation. We demonstrate utility of our approach on both classification and query retrieval tasks for time series and other types of multimedia data, then show that its incorporating into the relevance feedback system and query refinement can further improve the precision/recall by a wide margin.