Authors:
Petra Kubernátová
1
;
Magda Friedjungová
2
and
Max van Duijn
1
Affiliations:
1
Leiden Institute of Advanced Computer Science, Leiden University and Netherlands
;
2
Faculty of Information Technology, Czech Technical University in Prague and Czech Republic
Keyword(s):
Data Visualization, Recommender System, Non-experts, Model.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Computer Vision, Visualization and Computer Graphics
;
Data Analytics
;
Data Engineering
;
Data Management and Quality
;
Data Modeling and Visualization
;
General Data Visualization
;
Information and Scientific Visualization
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Symbolic Systems
Abstract:
In today’s age, there are huge amounts of data being generated every second of every day. Through data visualization, humans can explore, analyse and present it. Choosing a suitable visualization for data is a difficult task, especially for non-experts. Current data visualization recommender systems exist to aid in choosing a visualization, yet suffer from issues such as low accessibility and indecisiveness. The aim of this study is to create a model for a data visualization recommender system for non-experts that resolves these issues. Based on existing work and a survey among data scientists, requirements for a new model were identified and implemented. The result is a question-based model that uses a decision tree and a data visualization classification hierarchy in order to recommend a visualization. Furthermore, it incorporates both task-driven and data characteristics-driven perspectives, whereas existing solutions seem to either convolute these or focus on one of the two exclu
sively. Based on testing against existing solutions, it is shown that the new model reaches similar results while being simpler, clearer, more versatile, extendable and transparent. The presented model can be applied in the development of new data visualization software or as part of a learning tool.
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