On the Effectiveness of Distance Measures for Similarity Search in Multi-Variate Sensory Data: Effectiveness of Distance Measures for Similarity Search

Abstract

Integration of rich sensor technologies with everyday applications, such as gesture recognition and health monitoring, has raised the importance of the ability to effectively search and analyze multi-variate 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 effective when analyzing multi-variate 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 effectiveness of multi-variate time series distance measures against human motion data sets.