An Interactive Visualization Approach to Tackle Design Constraints in a Rule-Based Recommendation System

So far, existing rule-based frameworks that encode visualization design guidelines are eithertoo complex, laborious to maintain and extend, or produce negligible results. Therefore,Draco has been developed by Dominik Moritz et al. - an automated visualization recommendation system formalizing design knowledge as logical constraints in Answer SetProgramming (ASP). It is supposed to close the gap between visualization guidelines and their application in visualization tools. However, taking all advantages and capabilities of Draco into account, with an increasing set of constraints and incorporated designknowledge, even visualization experts lose overview and struggle to retrace the automated recommendation decisions made by the system.This thesis proposes an interactive visualization approach to Draco’s constraints. It issupposed to enable visualization experts to accomplish identified tasks regarding the knowledge-base and support them in understanding the system. To acquire the necessary data for this visualization approach, 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 forthis 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. In addition, we suggest a split but connected view of Draco’s visualization recommendations and our visualization. This faceted view is supposed to enable visualization experts to interactively explore the design rules’ violations based on highlighting respective constraints or recommendations.The implemented prototype verifies the feasibility of the data extraction strategy and the proposed visualization approach. An evaluation of the prototype combining qualitative and quantitative methods reveals open difficulties and misleading representations.However, the evaluation results also confirm the prototype’s effectiveness and value inacquiring insights into Draco’s recommendation process and design constraints.