CrossSet: Unveiling the Complex Interplay of Two Set-typed Dimensions in Multivariate Data

TL;DR

CrossSet is a novel interactive visualization technique that enables detailed multi-scale exploration of the complex interactions between two set-typed data dimensions, facilitating insights into their relationships and individual attributes.

Contribution

The paper introduces CrossSet, a hierarchical matrix layout for joint visualization of two set-typed dimensions, allowing multi-scale analysis of their interplay in a compact and interactive manner.

Findings

Effective in analyzing interactions between set-typed dimensions

Supports multi-scale exploration from overview to detail

Demonstrated success in multiple application scenarios

Abstract

The interactive visual analysis of set-typed data, i.e., data with attributes that are of type set, is a rewarding area of research and applications. Valuable prior work has contributed solutions that enable the study of such data with individual set-typed dimensions. In this paper, we present CrossSet, a novel method for the joint study of two set-typed dimensions and their interplay. Based on a task analysis, we describe a new, multi-scale approach to the interactive visual exploration and analysis of such data. Two set-typed data dimensions are jointly visualized using a hierarchical matrix layout, enabling the analysis of the interactions between two set-typed attributes at several levels, in addition to the analysis of individual such dimensions. CrossSet is anchored at a compact, large-scale overview that is complemented by drill-down opportunities to study the relations between and within the set-typed dimensions, enabling an interactive visual multi-scale exploration and analysis of bivariate set-typed data. Such an interactive approach makes it possible to study single set-typed dimensions in detail, to gain an overview of the interaction and association between two such dimensions, to refine one of the dimensions to gain additional details at several levels, and to drill down to the specific interactions of individual set-elements from the set-typed dimensions. To demonstrate the effectiveness and efficiency of CrossSet, we have evaluated the new method in the context of several application scenarios.