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Efficient Processing of Warping Time Series Join of Motion Capture Data
"... Abstract — Discovering nontrivial matching subsequences from two time series is very useful in synthesizing novel time series. This can be applied to applications such as motion synthesis where smooth and natural motion sequences are often required to be generated from existing motion sequences. We ..."
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Abstract — Discovering nontrivial matching subsequences from two time series is very useful in synthesizing novel time series. This can be applied to applications such as motion synthesis where smooth and natural motion sequences are often required to be generated from existing motion sequences. We first address this problem by defining it as a problem of lεjoin over two time series. Given two time series, the goal of lεjoin is to find those nontrivial matching subsequences by detecting maximal lconnections from the εmatching matrix of the two time series. Given a querying motion sequence, the lεjoin can be applied to retrieve all connectable motion sequences from a database of motion sequences. To support efficient lεjoin of time series, we propose a twostep filterandrefine algorithm, called Warping Time Series Join (WTSJ) algorithm. The filtering step serves to prune those sparse regions of the εmatching matrix where there are no maximal lconnections without incurring costly computation. The refinement step serves to detect closed lconnections within regions that cannot be pruned by the filtering step. To speed up the computation of εmatching matrix, we propose a blockbased time series summarization method, based on which the blockwise εmatching matrix is first computed. Lots of pairwise distance computation of elements can then be avoided by applying the filtering algorithm on the blockwise εmatching matrix. Extensive experiments on lεjoin of motion capture sequences are conducted. The results confirm the efficiency and effectiveness of our proposed algorithm in processing lεjoin of motion capture time series. I.