A genetic algorithm for automatic dashboard generation: first results

Praveen Soni (1, 2) , Cyril de Runz (2) , Fatma Bouali , Gilles Venturini (3)

1 RFAI - Reconnaissance des formes et analyse d'images
2 BDTLN - Bases de données et traitement des langues naturelles
3 LIFAT - Laboratoire d'Informatique Fondamentale et Appliquée de Tours

In this paper, we present a method for the automatic generation of dashboards (DBo) using a genetic algorithm (GA). A DBo is a set of visualizations, with possible linkage, intended to help users explore and analyse a dataset. Our Automatic Dashboard Generation System (ADGS) considers several input models for data, user and visualizations. We propose to represent a solution (i.e. a DBo) as a variable size matrix in which rows are visualizations and columns are data attributes. This representation can be evolved with a GA. For this purpose, we define genetic operators for DBo such as random generation, crossover, and mutation. We propose a fitness function to evaluate the quality of a DBo, as well as selection and replacement schemes. Finally, we present results with a benchmark dataset and a given user scenario. We show that the GA can find DBo that maximizes the evaluation function and that can be interesting for novice users. In perspectives, we will improve further the GA and we will study how to automatically propose a layout of the optimized DBo.

Mots clés: Dashboard Generation Novice users Genetic Algorithm Models (data user visualization); Dashboard Generation; Novice users; Genetic Algorithm; Models (data; user; visualization)

Domaines: Informatique [cs]

Praveen Soni, Cyril de Runz, Fatma Bouali, Gilles Venturini. A genetic algorithm for automatic dashboard generation: first results. 27 International Conference Information Visualisation, Jul 2023, Tampare, Finland. pp.77-82, ⟨10.1109/IV60283.2023.00023⟩. ⟨hal-04183569⟩