Omar U. Florez
Curtis Dyreson
ABSTRACT
Timeseries can be similar in shape but differ in length. For example, the sound waves produced by the same word spoken twice have roughly the same shape, but one may be shorter in duration. Stream data mining, approximate querying of image and video databases, data compression, and near duplicate detection are applications that need to be able to classify or cluster such timeseries, and to search for and rank timeseries that are similar to a chosen timeseries. We demonstrate software for clustering and performing similarity search in databases of timeseries data, where the timeseries have high and variable dimensionality. Our demonstration uses Timeseries Sensitive Hashing (TSH)[3] to index the timeseries. TSH adapts Locality Sensitive Hashing (LSH), which is an approximate algorithm to index data points in a d-dimensional space under some (e.g., Euclidean) distance function. TSH, unlike LSH, can index points that do not have the same dimensionality. As examples of the potential of TSH, the demonstration will index and classify timeseries from an image database and timeseries describing human motion extracted from a video stream and a motion capture system.
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Digital Library
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Index Terms
- Scalable similarity search of timeseries with variable dimensionality
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