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

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

doi:10.1145/2396761.2398747

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 proceeding › Conference 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 language	English (US)
Title of host publication	CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management
Pages	2743-2745
Number of pages	3
DOIs	https://doi.org/10.1145/2396761.2398747
State	Published - Dec 19 2012
Event	21st ACM International Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, United States Duration: Oct 29 2012 → Nov 2 2012

Publication series

Name	ACM International Conference Proceeding Series

Other

Other	21st ACM International Conference on Information and Knowledge Management, CIKM 2012
Country	United States
City	Maui, HI
Period	10/29/12 → 11/2/12

Keywords

data exploration
time series data sets

ASJC Scopus subject areas

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

Access to Document

10.1145/2396761.2398747

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STFMap : Query- and feature-driven visualization of large time series data sets. / Candan, Kasim; Rossini, Rosaria; Sapino, Maria Luisa; Wang, Xiaolan.

CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management. 2012. p. 2743-2745 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Candan, K, Rossini, R, Sapino, ML & 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. ACM International Conference Proceeding Series, pp. 2743-2745, 21st ACM International Conference on Information and Knowledge Management, CIKM 2012, Maui, HI, United States, 10/29/12. https://doi.org/10.1145/2396761.2398747

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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.",

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author = "Kasim Candan and Rosaria Rossini and Sapino, {Maria Luisa} and Xiaolan Wang",

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AB - 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.

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

Abstract

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Keywords

ASJC Scopus subject areas

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