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Motion capture data digitally represent human movements by sequences of body configurations in time. Subsequence searching in long sequences of such spatio-temporal data is difficult as query-relevant motions can vary in execution speeds and styles and can occur anywhere in a very long data sequence. To deal with these problems, we employ a fast and effective similarity measure that is elastic. The property of elasticity enables matching of two overlapping but slightly misaligned subsequences with a high confidence. Based on the elasticity, the long data sequence is partitioned into overlapping segments that are organized in multiple levels. The number of levels and sizes of overlaps are optimized to generate a modest number of segments while being able to trace an arbitrary query. In a retrieval phase, a query is always represented as a single segment and fast matched against segments within a relevant …