TY - UNPB
T1 - Grouping time-varying data for interactive exploration
AU - van Goethem, Arthur
AU - van Kreveld, Marc
AU - Löffler, Maarten
AU - Speckmann, Bettina
AU - Staals, Frank
N1 - Full version of our SoCG 2016 paper
PY - 2016/3/20
Y1 - 2016/3/20
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 Rd 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 Rd for fixed d.
U2 - 10.48550/arXiv.1603.06252
DO - 10.48550/arXiv.1603.06252
M3 - Preprint
SP - 1
EP - 23
BT - Grouping time-varying data for interactive exploration
PB - arXiv
ER -