Brussels, Belgium Belgium
Dec. 10, 2012 to Dec. 10, 2012
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2012.27
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.
Time series analysis, Search problems, Approximation methods, Indexing, Vegetation, Runtime, data analysis, time series, similarity search, earth science, rare class
Ivan Brugere, Karsten Steinhaeuser, Shyam Boriah, Vipin Kumar, "Approximate Search on Massive Spatiotemporal Datasets", ICDMW, 2012, 2013 IEEE 13th International Conference on Data Mining Workshops, 2013 IEEE 13th International Conference on Data Mining Workshops 2012, pp. 773-780, doi:10.1109/ICDMW.2012.27