On the Eectiveness of Distance Measures for Similarity Search in Multi-Variate Sensory Data∗

Integration of rich sensor technologies with everyday applications, such as gesture recognition and health monitoring, has raised the importance of the ability to e‚ectively search and analyze multivariate time series data. Consequently, various time series distance measures (such as Euclidean distance, edit distance, and dynamic time warping) have been extended from uni-variate to multi-variate time series. In this paper, we note that the naive extensions of these measures may not necessarily be e‚ective when analyzing multivariate time series data. We present several algorithms, some of which leverage external metadata describing the potential relationships, either learned from the data or captured from the metadata, among the variates. We then experimentally study the e‚ectiveness of multi-variate time series distance measures against human motion data sets. CCS CONCEPTS •Information systems →Similarity measures; Multimedia and multimodal retrieval;