  Storyline visualizations are a popular way of visualizing characters and
their interactions over time: Characters are drawn as x-monotone curves and
interactions are visualized through close proximity of the corresponding
character curves in a vertical strip. Existing methods to generate storylines
assume a total ordering of the interactions, although real-world data often do
not contain such a total order. Instead, multiple interactions are often
grouped into coarser time intervals such as years. We exploit this grouping
property by introducing a new model called storylines with time intervals and
present two methods to minimize the number of crossings and horizontal space
usage. We then evaluate these algorithms on a small benchmark set to show their
effectiveness.
