# Crossing Minimization in Time Interval Storylines

## TL;DR

This paper introduces new models and algorithms for creating storyline visualizations that handle grouped time intervals, aiming to reduce crossings and space usage, and evaluates their effectiveness on benchmarks.

## Contribution

It proposes a novel model for storylines with time intervals and two algorithms to minimize crossings and space, addressing limitations of previous methods assuming total orderings.

## Key findings

- Algorithms effectively reduce crossings in storylines.
- Methods decrease horizontal space in visualizations.
- Evaluation shows improved visualization clarity.

## Abstract

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.