Approximate search on massive spatiotemporal datasets.

Loading...
Thumbnail Image

Persistent link to this item

Statistics
View Statistics

Journal Title

Journal ISSN

Volume Title

Title

Approximate search on massive spatiotemporal datasets.

Published Date

2012-08

Publisher

Type

Thesis or Dissertation

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. This thesis formulates a simple approximate range query problem for time series data, and proposes a method that aims to quickly access a small number of high-quality results of the exact search resultset. We formulate an anytime framework, giving the user flexibility to return query results in arbitrary cost, where larger runtime incrementally improves search results. We propose an evaluation strategy on each 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.

Description

University of Minnesota M.S. thesis. August 2012. Major: Computer science. Advisor: Professor Vipin Kumar. 1 computer file (PDF); vi, 44 pages.

Related to

Replaces

License

Series/Report Number

Funding information

Isbn identifier

Doi identifier

Previously Published Citation

Suggested citation

Brugere, Ivan. (2012). Approximate search on massive spatiotemporal datasets.. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/139992.

Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.