Approximate Search on Massive Spatiotemporal Datasets

Efficient time series similarity search is a fundamental operation for data exploration and analysis. While previous work has focused on indexing progressively larger datasets and has proposed data structures with efficient exact search algorithms, we motivate the need for approximate query methods that can be used in interactive exploration and as fast data analysis subroutines on large spatiotemporal datasets. We formulate a simple approximate range query problem for time series data, and propose a method that aims to quickly access a small number of high quality results of the exact search result set. We propose an evaluation strategy on the query framework when the false dismissal class is very large relative to the query result set, and investigate the performance of indexing novel classes of time series subsequences.