GEViTRec: Data Reconnaissance Through Recommendation Using a Domain-Specific Prevalence Visualization Design Space

by   Anamaria Crisan, et al.

Genomic Epidemiology (genEpi) is a branch of public health that uses many different data types including tabular, network, genomic, and geographic, to identify and contain outbreaks of deadly diseases. Due to the volume and variety of data, it is challenging for genEpi domain experts to conduct data reconnaissance; that is, have an overview of the data they have and make assessments toward its quality, completeness, and suitability. We present an algorithm for data reconnaissance through automatic visualization recommendation, GEViTRec. Our approach handles a broad variety of dataset types and automatically generates coordinated combinations of charts, in contrast to existing systems that primarily focus on singleton visual encodings of tabular datasets. We automatically detect linkages across multiple input datasets by analyzing non-numeric attribute fields, creating an entity graph within which we analyze and rank paths. For each high-ranking path, we specify chart combinations with spatial and color alignments between shared fields, using a gradual binding approach to transform initial partial specifications of singleton charts to complete specifications that are aligned and oriented consistently. A novel aspect of our approach is its combination of domain-agnostic elements with domain-specific information that is captured through a domain-specific visualization prevalence design space. Our implementation is applied to both synthetic data and real data from an Ebola outbreak. We compare GEViTRec's output to what previous visualization recommendation systems would generate, and to manually crafted visualizations used by practitioners. We conducted formative evaluations with ten genEpi experts to assess the relevance and interpretability of our results.


page 1

page 9

page 10

page 16


Insight-centric Visualization Recommendation

Visualization recommendation systems simplify exploratory data analysis ...

Recommendations for Visualization Recommendations: Exploring Preferences and Priorities in Public Health

The promise of visualization recommendation systems is that analysts wil...

Are We There Yet? A Review on Existing Perceptual Theory and Experiment Support for Visualization Recommendation Systems

A growing body of research focuses on helping users explore complex data...

KG4Vis: A Knowledge Graph-Based Approach for Visualization Recommendation

Visualization recommendation or automatic visualization generation can s...

Exploring the Design Space of Aesthetics with the Repertory Grid Technique

By optimizing aesthetics, graph diagrams can be generated that are easie...

VizML: A Machine Learning Approach to Visualization Recommendation

Data visualization should be accessible for all analysts with data, not ...

Projectional Editors for JSON-Based DSLs

Augmenting text-based programming with rich structured interactions has ...

Please sign up or login with your details

Forgot password? Click here to reset