HCGRAug 2, 2021

SightBi: Exploring Cross-View Data Relationships with Biclusters

arXiv:2108.01044v22 citations
AI Analysis

This addresses the challenge for analysts in domains like bioinformatics and cybersecurity who need to efficiently link data across different views, though it appears incremental as it builds on existing view-coordination methods.

The paper tackles the problem of exploring cross-view data relationships in multiple-view visualization, which currently requires significant user effort through trial-and-error coordination techniques, by presenting SightBi, a visual analytics approach that formalizes these relationships as biclusters and uses a bi-context design to guide exploration, as demonstrated in a usage scenario.

Multiple-view visualization (MV) has been heavily used in visual analysis tools for sensemaking of data in various domains (e.g., bioinformatics, cybersecurity and text analytics). One common task of visual analysis with multiple views is to relate data across different views. For example, to identify threats, an intelligence analyst needs to link people from a social network graph with locations on a crime-map, and then search for and read relevant documents. Currently, exploring cross-view data relationships heavily relies on view-coordination techniques (e.g., brushing and linking), which may require significant user effort on many trial-and-error attempts, such as repetitiously selecting elements in one view, and then observing and following elements highlighted in other views. To address this, we present SightBi, a visual analytics approach for supporting cross-view data relationship explorations. We discuss the design rationale of SightBi in detail, with identified user tasks regarding the use of cross-view data relationships. SightBi formalizes cross-view data relationships as biclusters, computes them from a dataset, and uses a bi-context design that highlights creating stand-alone relationship-views. This helps preserve existing views and offers an overview of cross-view data relationships to guide user exploration. Moreover, SightBi allows users to interactively manage the layout of multiple views by using newly created relationship-views. With a usage scenario, we demonstrate the usefulness of SightBi for sensemaking of cross-view data relationships.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

Your Notes