STFMap: Query- and feature-driven visualization of large time series data sets

Kasim Candan, Rosaria Rossini, Maria Luisa Sapino, Xiaolan Wang

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

1 Scopus citations

Abstract

Since many applications rely on time-based data, visualizing temporal data and helping experts explore large time series data sets are critical in many application domains. In this interactive system preview, we argue that time series often carry structural features that can, if efficiently identified and effectively visualized, help reduce visual overload and help the user quickly focus on the relevant portions of the data sets. Relying on this observation, we introduce a novel STFMap system, which includes four innovative query- and feature-driven time series data set visualization techniques: (a) segment-maps, (b) warp-maps, (c) stretch-maps, and (d) feature-maps. These rely on the salient temporal features of the time series and their alignments with respect to the given user query to help users explore the data set in a query-driven fashion.

Original languageEnglish (US)
Title of host publicationCIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management
Pages2743-2745
Number of pages3
DOIs
StatePublished - Dec 19 2012
Event21st ACM International Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, United States
Duration: Oct 29 2012Nov 2 2012

Publication series

NameACM International Conference Proceeding Series

Other

Other21st ACM International Conference on Information and Knowledge Management, CIKM 2012
CountryUnited States
CityMaui, HI
Period10/29/1211/2/12

Keywords

  • data exploration
  • time series data sets

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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    Candan, K., Rossini, R., Sapino, M. L., & Wang, X. (2012). STFMap: Query- and feature-driven visualization of large time series data sets. In CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management (pp. 2743-2745). (ACM International Conference Proceeding Series). https://doi.org/10.1145/2396761.2398747