Time Curves: Folding Time to Visualize Patterns of Temporal Evolution in Data
We introduce time curves as a general approach for visualizing patterns of evolution in temporal data. Examples of such patterns include slow and regular progressions, large sudden changes, and reversals to previous states. These patterns can be of interest in a range of domains, such as collaborati...
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| Veröffentlicht in: | IEEE transactions on visualization and computer graphics Jg. 22; H. 1; S. 559 - 568 |
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| Hauptverfasser: | , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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United States
IEEE
01.01.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers |
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| ISSN: | 1077-2626, 1941-0506, 1941-0506 |
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| Abstract | We introduce time curves as a general approach for visualizing patterns of evolution in temporal data. Examples of such patterns include slow and regular progressions, large sudden changes, and reversals to previous states. These patterns can be of interest in a range of domains, such as collaborative document editing, dynamic network analysis, and video analysis. Time curves employ the metaphor of folding a timeline visualization into itself so as to bring similar time points close to each other. This metaphor can be applied to any dataset where a similarity metric between temporal snapshots can be defined, thus it is largely datatype-agnostic. We illustrate how time curves can visually reveal informative patterns in a range of different datasets. |
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| AbstractList | We introduce time curves as a general approach for visualizing patterns of evolution in temporal data. Examples of such patterns include slow and regular progressions, large sudden changes, and reversals to previous states. These patterns can be of interest in a range of domains, such as collaborative document editing, dynamic network analysis, and video analysis. Time curves employ the metaphor of folding a timeline visualization into itself so as to bring similar time points close to each other. This metaphor can be applied to any dataset where a similarity metric between temporal snapshots can be defined, thus it is largely datatype-agnostic. We illustrate how time curves can visually reveal informative patterns in a range of different datasets.We introduce time curves as a general approach for visualizing patterns of evolution in temporal data. Examples of such patterns include slow and regular progressions, large sudden changes, and reversals to previous states. These patterns can be of interest in a range of domains, such as collaborative document editing, dynamic network analysis, and video analysis. Time curves employ the metaphor of folding a timeline visualization into itself so as to bring similar time points close to each other. This metaphor can be applied to any dataset where a similarity metric between temporal snapshots can be defined, thus it is largely datatype-agnostic. We illustrate how time curves can visually reveal informative patterns in a range of different datasets. We introduce time curves as a general approach for visualizing patterns of evolution in temporal data. Examples of such patterns include slow and regular progressions, large sudden changes, and reversals to previous states. These patterns can be of interest in a range of domains, such as collaborative document editing, dynamic network analysis, and video analysis. Time curves employ the metaphor of folding a timeline visualization into itself so as to bring similar time points close to each other. This metaphor can be applied to any dataset where a similarity metric between temporal snapshots can be defined, thus it is largely datatype-agnostic. We illustrate how time curves can visually reveal informative patterns in a range of different datasets. |
| Author | Conglei Shi Grabowski, Tom Dragicevic, Pierre Heulot, Nicolas Bach, Benjamin Madhyastha, Tara |
| Author_xml | – sequence: 1 givenname: Benjamin surname: Bach fullname: Bach, Benjamin email: benj.bach@gmail.com organization: Microsoft Res.-Inria Joint Centre, USA – sequence: 2 surname: Conglei Shi fullname: Conglei Shi email: shiconglei@gmail.com organization: T.J. Watson Res. Center, IBM, Yorktown Heights, NY, USA – sequence: 3 givenname: Nicolas surname: Heulot fullname: Heulot, Nicolas email: nicolas.heulot@irt-systemx.fr – sequence: 4 givenname: Tara surname: Madhyastha fullname: Madhyastha, Tara email: madhyt@u.washington.edu organization: Dept. of Radiol., Univ. of Washington, Seattle, WA, USA – sequence: 5 givenname: Tom surname: Grabowski fullname: Grabowski, Tom email: tgrabow@u.washington.edu organization: Dept. of Radiol. & Neurology, Univ. of Washington, Seattle, WA, USA – sequence: 6 givenname: Pierre surname: Dragicevic fullname: Dragicevic, Pierre email: pierre.dragice@gmail.com |
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| Cites_doi | 10.1371/journal.pcbi.1000072 10.1145/985692.985765 10.1023/B:VISI.0000029664.99615.94 10.1016/j.neuroimage.2011.12.028 10.1109/TVCG.2007.70535 10.1109/TVCG.2014.2346325 10.1145/1753326.1753427 10.1109/TIT.2004.838101 10.1111/j.1467-8659.2009.01687.x 10.1109/WSC.2003.1261490 10.20982/tqmp.05.1.p001 10.1145/1357054.1357096 10.1016/j.neuroimage.2011.10.018 10.1145/2634274.2634277 10.1109/INFVIS.2002.1173155 10.1007/BF02288916 10.1142/6725 10.1016/j.neuroimage.2013.05.079 10.1145/2254556.2254654 10.1007/978-3-319-11593-1_2 10.1007/978-0-85729-079-3 10.1109/PACIFICVIS.2010.5429611 10.1007/978-3-540-70904-6_44 10.1109/TVCG.2013.192 10.1109/INFVIS.2004.1 10.1142/S0218001404003228 10.1145/2470654.2470724 10.1016/j.neuroimage.2013.08.048 10.1111/cgf.12615 10.1007/BF02289630 10.1111/j.1467-8659.2012.03158.x 10.1145/1979742.1979570 10.1109/ICCV.2003.1238658 10.1109/VL.1996.545307 10.1109/TVCG.2011.186 10.1109/MCG.2003.1242376 10.1016/j.patcog.2006.04.045 |
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| Title | Time Curves: Folding Time to Visualize Patterns of Temporal Evolution in Data |
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