C2Views: Knowledge-based Colormap Design for Multiple-View Consistency

TL;DR

C2Views introduces a knowledge-based, optimization-driven framework for designing colormaps that enhance multiple-view consistency and data distinction in complex visualizations, with user-guided refinement.

Contribution

The paper presents a novel framework that implicitly encodes view relationships into colormap design using a knowledge graph and genetic algorithms, improving multi-view visual consistency.

Findings

Outperforms existing methods in color distinction and multi-view consistency

Demonstrates adaptability across diverse data relationships and view layouts

User studies confirm improved data exploration efficiency

Abstract

Multiple-view (MV) visualization provides a comprehensive and integrated perspective on complex data, establishing itself as an effective method for visual communication and exploratory data analysis. While existing studies have predominantly focused on designing explicit visual linkages and coordinated interactions to facilitate the exploration of MV visualizations, these approaches often demand extra graphical and interactive effort, overlooking the potential of color as an effective channel for encoding data and relationships. Addressing this oversight, we introduce C2Views, a new framework for colormap design that implicitly shows the relation across views. We begin by structuring the components and their relationships within MVs into a knowledge-based graph specification, wherein colormaps, data, and views are denoted as entities, and the interactions among them are illustrated as relations. Building on this representation, we formulate the design criteria as an optimization problem and employ a genetic algorithm enhanced by Pareto optimality, generating colormaps that balance single-view effectiveness and multiple-view consistency. Our approach is further complemented with an interactive interface for user-intended refinement. We demonstrate the feasibility of C2Views through various colormap design examples for MVs, underscoring its adaptability to diverse data relationships and view layouts. Comparative user studies indicate that our method outperforms the existing approach in facilitating color distinction and enhancing multiple-view consistency, thereby simplifying data exploration processes.