Approximate search on massive spatiotemporal datasets

Ivan Brugere, Karsten Steinhaeuser, Shyam Boriah, Vipin Kumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 resultset.We propose an evaluation strategy on the query framework when the false dismissal class is very large relative to the query resultset, and investigate the performance of indexing novel classes of time series subsequences.

Original languageEnglish (US)
Title of host publicationProceedings - 12th IEEE International Conference on Data Mining Workshops, ICDMW 2012
Pages773-780
Number of pages8
DOIs
StatePublished - Dec 1 2012
Event12th IEEE International Conference on Data Mining Workshops, ICDMW 2012 - Brussels, Belgium
Duration: Dec 10 2012Dec 10 2012

Publication series

NameProceedings - 12th IEEE International Conference on Data Mining Workshops, ICDMW 2012

Other

Other12th IEEE International Conference on Data Mining Workshops, ICDMW 2012
Country/TerritoryBelgium
CityBrussels
Period12/10/1212/10/12

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