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 | ACM International Conference Proceeding Series |
| Pages | 2743-2745 |
| Number of pages | 3 |
| DOIs | |
| State | Published - 2012 |
| Event | 21st ACM International Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, United States Duration: Oct 29 2012 → Nov 2 2012 |
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 |
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Keywords
- data exploration
- time series data sets
ASJC Scopus subject areas
- Human-Computer Interaction
- Computer Networks and Communications
- Computer Vision and Pattern Recognition
- Software
Cite this
STFMap : Query- and feature-driven visualization of large time series data sets. / Candan, Kasim; Rossini, Rosaria; Sapino, Maria Luisa; Wang, Xiaolan.
ACM International Conference Proceeding Series. 2012. p. 2743-2745.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - STFMap
T2 - Query- and feature-driven visualization of large time series data sets
AU - Candan, Kasim
AU - Rossini, Rosaria
AU - Sapino, Maria Luisa
AU - Wang, Xiaolan
PY - 2012
Y1 - 2012
N2 - 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.
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.
KW - data exploration
KW - time series data sets
UR - http://www.scopus.com/inward/record.url?scp=84871057425&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84871057425&partnerID=8YFLogxK
U2 - 10.1145/2396761.2398747
DO - 10.1145/2396761.2398747
M3 - Conference contribution
SN - 9781450311564
SP - 2743
EP - 2745
BT - ACM International Conference Proceeding Series
ER -