University of Helsinki: Chen He defends her PhD thesis on Entity-Based Insight Discovery in Visual Data Exploration

M.Sc. Chen He defends her doctoral thesis Entity-Based Insight Discovery in Visual Data Exploration on Thursday the 27th of January 2022 at 16 o’clock in the University of Helsinki Porthania building, Hall PIII (Yliopistonkatu 3, 1st floor). Her opponent is Associate Professor T.J. Jankun-Kelly (Mississippi State University, USA) and custos Professor Giulio Jacucci (University of Helsinki). The defence will be held in English. It is possible to follow the defence as a live stream at https://helsinki.zoom.us/j/69181597081.

The thesis of Chen He is a part of research done in the Department of Computer Science and in the Ubiquitous Interaction group at the University of Helsinki. Her supervisor has been Professor Giulio Jacucci (University of Helsinki).

Entity-Based Insight Discovery in Visual Data Exploration
Visual data exploration (VDE) allows the human to get insight into the data via interaction with visual depictions of that data. Despite the state-of-the-art visualization design models and evaluation methods proposed to support VDE, the community still lacks an understanding of interaction design in visualization and how users extract insight through interacting with the data. This research aims to address these two challenges.

For interaction design, a literature review reveals that a lack of actionability hinders the application of existing visualization design methods. To address this challenge, this research proposes an approach abstracting data to entities and designing entity-based interactions to achieve the higher-level interaction goals. Three case studies, i.e., interacting with information facets to support fluid exploratory search, interacting with drug-target relations for insight discovery and sharing, and supporting insight externalization through references to visualization components, demonstrate the applicability of this approach in practice. The three cases detail how the approach could address the design requirements derived from related work to fulfill the various task goals following the nested model of visualization design and the resulting designs’ transferability to other datasets. Reflecting on the case studies, we provide design guidelines to help improve the entity-based interaction design.

To understand the insight generation process of VDE, we present two user studies asking users to explore a visualization tool and externalize insights by inputting notes. We logged user interactions and characterized collected insights for correlation and prediction analysis. Correlation analysis of the first study showed that exploration actions tended to relate to unexpected insights; the drill-down interaction pattern could lead to insights with higher domain values. Besides asking users to input notes as insights, the second study enabled users to refer to relevant entities (visualization components and prior notes) to assist their narration. Results showed evidence that entity references provided better predictions than interactions on insight characteristics (category, overview versus detail, and using prior knowledge). We discuss study limitations and results’ implications on knowledge-assisted visualization, such as supporting insight recommendations.

As future work, structuring user notes by entities could make the insight machine-readable to stimulate mixed-initiative exploration, e.g., machines help to collect evidence to validate the insight. Creating a platform that supports uncertainty-aware insight and insight provenance across tools could facilitate practical analysis which usually involves multiple analysis tools.

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