Grouping time-varying data for interactive exploration

A.I. van Goethem; M.J. van Kreveld; M. Löffler; B. Speckmann; F. Staals

doi:10.4230/LIPIcs.SoCG.2016.61

Grouping time-varying data for interactive exploration

A.I. van Goethem, M.J. van Kreveld, M. Löffler, B. Speckmann, F. Staals

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

6 Citations (Scopus)

52 Downloads (Pure)

Abstract

We present algorithms and data structures that support the interactive analysis of the grouping structure of one-, two-, or higher-dimensional time-varying data while varying all defining parameters. Grouping structures characterise important patterns in the temporal evaluation of sets of time-varying data. We follow Buchin et al. [JoCG 2015] who define groups using three parameters: group-size, group-duration, and inter-entity distance. We give upper and lower bounds on the number of maximal groups over all parameter values, and show how to compute them efficiently. Furthermore, we describe data structures that can report changes in the set of maximal groups in an output-sensitive manner. Our results hold in R^d for fixed d.

Original language	English
Title of host publication	Proc. of the 32nd annual Symposium on Computational Geometry (SoCG)
Publisher	Dagstuhl Publishing
Pages	1-16
DOIs	https://doi.org/10.4230/LIPIcs.SoCG.2016.61
Publication status	Published - 2016
Event	32nd International Symposium on Computational Geometry (SoCG 2016) - Boston, United States Duration: 14 Jun 2016 → 18 Jun 2016

Conference

Conference	32nd International Symposium on Computational Geometry (SoCG 2016)
Country/Territory	United States
City	Boston
Period	14/06/16 → 18/06/16

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10.4230/LIPIcs.SoCG.2016.61

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title = "Grouping time-varying data for interactive exploration",

abstract = "We present algorithms and data structures that support the interactive analysis of the grouping structure of one-, two-, or higher-dimensional time-varying data while varying all defining parameters. Grouping structures characterise important patterns in the temporal evaluation of sets of time-varying data. We follow Buchin et al. [JoCG 2015] who define groups using three parameters: group-size, group-duration, and inter-entity distance. We give upper and lower bounds on the number of maximal groups over all parameter values, and show how to compute them efficiently. Furthermore, we describe data structures that can report changes in the set of maximal groups in an output-sensitive manner. Our results hold in R^d for fixed d.",

author = "{van Goethem}, A.I. and {van Kreveld}, M.J. and M. L{\"o}ffler and B. Speckmann and F. Staals",

year = "2016",

doi = "10.4230/LIPIcs.SoCG.2016.61",

language = "English",

pages = "1--16",

booktitle = "Proc. of the 32nd annual Symposium on Computational Geometry (SoCG)",

publisher = "Dagstuhl Publishing",

note = "32nd International Symposium on Computational Geometry (SoCG 2016) ; Conference date: 14-06-2016 Through 18-06-2016",

}

van Goethem, AI, van Kreveld, MJ, Löffler, M, Speckmann, B & Staals, F 2016, Grouping time-varying data for interactive exploration. in Proc. of the 32nd annual Symposium on Computational Geometry (SoCG)., 61, Dagstuhl Publishing, pp. 1-16, 32nd International Symposium on Computational Geometry (SoCG 2016) , Boston, United States, 14/06/16. https://doi.org/10.4230/LIPIcs.SoCG.2016.61

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T1 - Grouping time-varying data for interactive exploration

AU - van Goethem, A.I.

AU - van Kreveld, M.J.

AU - Löffler, M.

AU - Speckmann, B.

AU - Staals, F.

PY - 2016

Y1 - 2016

N2 - We present algorithms and data structures that support the interactive analysis of the grouping structure of one-, two-, or higher-dimensional time-varying data while varying all defining parameters. Grouping structures characterise important patterns in the temporal evaluation of sets of time-varying data. We follow Buchin et al. [JoCG 2015] who define groups using three parameters: group-size, group-duration, and inter-entity distance. We give upper and lower bounds on the number of maximal groups over all parameter values, and show how to compute them efficiently. Furthermore, we describe data structures that can report changes in the set of maximal groups in an output-sensitive manner. Our results hold in R^d for fixed d.

AB - We present algorithms and data structures that support the interactive analysis of the grouping structure of one-, two-, or higher-dimensional time-varying data while varying all defining parameters. Grouping structures characterise important patterns in the temporal evaluation of sets of time-varying data. We follow Buchin et al. [JoCG 2015] who define groups using three parameters: group-size, group-duration, and inter-entity distance. We give upper and lower bounds on the number of maximal groups over all parameter values, and show how to compute them efficiently. Furthermore, we describe data structures that can report changes in the set of maximal groups in an output-sensitive manner. Our results hold in R^d for fixed d.

U2 - 10.4230/LIPIcs.SoCG.2016.61

DO - 10.4230/LIPIcs.SoCG.2016.61

M3 - Conference contribution

SP - 1

EP - 16

BT - Proc. of the 32nd annual Symposium on Computational Geometry (SoCG)

PB - Dagstuhl Publishing

T2 - 32nd International Symposium on Computational Geometry (SoCG 2016)

Y2 - 14 June 2016 through 18 June 2016

ER -

Grouping time-varying data for interactive exploration

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