ChartDetective: Easy and Accurate Interactive Data Extraction from Complex Vector Charts

Masson, Damien; Malacria, Sylvain; Vogel, Daniel; Lank, Edward; Casiez, Géry

Type de document :

Communication dans un congrès avec actes

DOI :

10.1145/3544548.3581113

Titre :

ChartDetective: Easy and Accurate Interactive Data Extraction from Complex Vector Charts

Auteur(s) :

Masson, Damien [Auteur]
Cheriton School of Computer Science [Waterloo] [CS]
Malacria, Sylvain [Auteur]

Technology and knowledge for interaction [LOKI]
Vogel, Daniel [Auteur]
Cheriton School of Computer Science [Waterloo] [CS]
Lank, Edward [Auteur]
Cheriton School of Computer Science [Waterloo] [CS]
Technology and knowledge for interaction [LOKI]
Casiez, Géry [Auteur]

Technology and knowledge for interaction [LOKI]

Titre de la manifestation scientifique :

CHI 2023 - ACM Conference on Human Factors in Computing Systems

Ville :

Hamburg

Pays :

Allemagne

Date de début de la manifestation scientifique :

2023-04-22

Titre de l’ouvrage :

Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2023)

Mot(s)-clé(s) en anglais :

data extraction
chart reverse-engineering
vector graphics
Human-Computer Interaction

Discipline(s) HAL :

Informatique [cs]/Interface homme-machine [cs.HC]

Résumé en anglais : [en]

Extracting underlying data from rasterized charts is tedious and inaccurate; values might be partially occluded or hard to distinguish, and the quality of the image limits the precision of the data being recovered. To ...
Lire la suite >Extracting underlying data from rasterized charts is tedious and inaccurate; values might be partially occluded or hard to distinguish, and the quality of the image limits the precision of the data being recovered. To address these issues, we introduce a semi-automatic system leveraging vector charts to extract the underlying data easily and accurately. The system is designed to make the most of vector information by relying on a drag-and-drop interface combined with selection, filtering, and previsualization features. A user study showed that participants spent less than 4 minutes to accurately recover data from charts published at CHI with diverse styles, thousands of data points, a combination of different encodings, and elements partially or completely occluded. Compared to other approaches relying on raster images, our tool successfully recovered all data, even when hidden, with a 78% lower relative error.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Projet ANR :

Promouvoir et améliorer la découverte des fonctionnalités et des interactions dans les systèmes interactifs

Collections :