An Augmented Visual Query Mechanism for Finding Patterns in Time Series Data
FQAS '02 Proceedings of the 5th International Conference on Flexible Query Answering Systems
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Visualizing and discovering non-trivial patterns in large time series databases
Information Visualization
A Bit Level Representation for Time Series Data Mining with Shape Based Similarity
Data Mining and Knowledge Discovery
Exact indexing of dynamic time warping
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
sDTW: computing DTW distances using locally relevant constraints based on salient feature alignments
Proceedings of the VLDB Endowment
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Since many applications rely on time-based data, visualizing temporal data and helping experts explore large time series data sets are critical in many application domains. In this interactive system preview, we argue that time series often carry structural features that can, if efficiently identified and effectively visualized, help reduce visual overload and help the user quickly focus on the relevant portions of the data sets. Relying on this observation, we introduce a novel STFMap system, which includes four innovative query- and feature-driven time series data set visualization techniques: (a) segment-maps, (b) warp-maps, (c) stretch-maps, and (d) feature-maps. These rely on the salient temporal features of the time series and their alignments with respect to the given user query to help users explore the data set in a query-driven fashion.