Honorable Mention

SightBi: Exploring Cross-View Data Relationships with Biclusters

Maoyuan Sun, Abdul Rahman Shaikh, Hamed Alhoori, Jian Zhao

View presentation: 2021-10-27T15:15:00Z GMT-0600 Change your timezone on the schedule page
2021-10-27T15:15:00Z
Exemplar figure, described by caption below
Abstract

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 and read relevant documents. Currently, exploring cross-view data relationships heavily relies on view-coordination techniques (e.g., brushing and linking). They may require significant user effort on many trial-and-error attempts, such as repetitiously selecting elements in one view, 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 usage of cross-view data relationships. SightBi formalize cross-view data relationships as biclusters and compute them from a dataset. SightBi uses a bi-context design that highlights creating stand-alone relationship-views. This helps to preserve existing views and serves as an overview of cross-view data relationships to guide user explorations. 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.