Multiform Visualization of Heterogeneous Data Spaces Master’s Thesis

Investigators from different domains are frequently confronted with large volumes of data originating from multiple sources that need to be analyzed simultaneously. Inhomogeneities within datasets thereby often obstruct the analysis process, but can also be the source potentially interesting relationships in the data. Most existing visualization techniques do not take such inhomogeneities into account and represent the data in a one-view-fits-all fashion, thus being of limited use in the course of an analysis. The proposed approach considers the inhomogeneous nature of datasets and the need for a differentiated visualization of their subsets. It is based on VisBricks, an interactive multiform visualization that is made up of basic building blocks called bricks. Each brick visualizes a homogeneous subset of a dataset in a way that suits the subset’s characteristics best. Visual linking of bricks is used to indicate relationships between different homogeneous subsets. VisBricks also provides drill-down features that allow a detailed examination of individual subsets. An important issue when dealing with several homogeneous subsets in a setup of multiple datasets is their configuration and management. For that purpose we developed the Data-View Integrator, which combines views and datasets in an abstract graph representation. It provides an overview of available datasets and their relationships, and can be used to configure and assign homogeneous subsets to VisBricks or other views for an in-depth analysis. The proposed visualization techniques were evaluated in case studies with domain experts in the context of multiple genomic datasets that are used for the characterization of cancer subtypes. The experts were able to quickly reproduce known results from the literature and also gained new insights into the data.