Visual Analytics for Understanding Draco's Knowledge Base

Johanna Schmidt, Bernhard Pointner, Silvia Miksch

Room: 105

2023-10-25T03:00:00ZGMT-0600Change your timezone on the schedule page
Exemplar figure, described by caption below
Visually inspecting Draco's recommendations. Our interactive Visual Analytics solution allows users to explore the set of rules the visualization recommendation system Draco is built on. On the left side, four recommended visualizations are shown. Every recommendation has costs assigned, which relates to how many rules have been violated by this recommendation. On the right, side we present our hypergraph-based visualization of the set of rules and constraints that are used by Draco. By selecting recommendations on the left (A, blue, and B, red), the rules violated by these visualizations are highlighted in the graph (red and blue dashed lines).
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Visual Analytics, Hypergraph visualization, Rule-based recommendation systems


Draco has been developed as an automated visualization recommendation system formalizing design knowledge as logical constraints in ASP (Answer-Set Programming). With an increasing set of constraints and incorporated design knowledge, even visualization experts lose overview in Draco and struggle to retrace the automated recommendation decisions made by the system. Our paper proposes an Visual Analytics (VA) approach to visualize and analyze Draco's constraints. Our VA approach is supposed to enable visualization experts to accomplish identified tasks regarding the knowledge base and support them in better understanding Draco. We extend the existing data extraction strategy of Draco with a data processing architecture capable of extracting features of interest from the knowledge base. A revised version of the ASP grammar provides the basis for this data processing strategy. The resulting incorporated and shared features of the constraints are then visualized using a hypergraph structure inside the radial-arranged constraints of the elaborated visualization. The hierarchical categories of the constraints are indicated by arcs surrounding the constraints. Our approach is supposed to enable visualization experts to interactively explore the design rules' violations based on highlighting respective constraints or recommendations. A qualitative and quantitative evaluation of the prototype confirms the prototype's effectiveness and value in acquiring insights into Draco's recommendation process and design constraints.