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Grouping Time-Varying Data for Interactive Exploration van Goethem, Arthur van Kreveld, Marc Löffler, Maarten Speckmann, Bettina Staals, Frank Trajectory Time series Moving entity Grouping Algorithm Data structure 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Arthur van Goethem and Marc van Kreveld and Maarten Löffler and Bettina Speckmann and Frank Staals 2016 Is Part Of LIPIcs, Volume 51, 32nd International Symposium on Computational Geometry (SoCG 2016) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.SoCG.2016.61 urn:nbn:de:0030-drops-59539 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SoCG.2016.61 eng https://creativecommons.org/licenses/by/3.0/legalcode