Grouping Time-varying Data for Interactive Exploration

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-varyi...

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Bibliographic Details
Published inarXiv.org
Main Authors Arthur van Goethem, Marc van Kreveld, Löffler, Maarten, Speckmann, Bettina, Staals, Frank
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 20.03.2016
Subjects
Algorithms
Data structures
Lower bounds
Parameters
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Summary: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 \(\mathbb{R}^d\) for fixed \(d\).
ISSN:2331-8422