Accéder directement au contenu Accéder directement à la navigation
Nouvelle interface
Communication dans un congrès

How Domain Experts Structure Their Exploratory Data Analysis: Towards a Machine-Learned Storyline

Antoine Barczewski 1 Anastasia Bezerianos 2 Nadia Boukhelifa 3 
2 ILDA - Interacting with Large Data
Inria Saclay - Ile de France, LISN - Laboratoire Interdisciplinaire des Sciences du Numérique, IaH - Interaction avec l'Humain
Abstract : Exploratory data analysis is an open-ended iterative process, where the goal is to discover new insights. Much of the work to characterise this exploration stems from qualitative research resulting in rich findings, task taxonomies, and conceptual models. In this work, we propose a machine-learning approach where the structure of an exploratory analysis session is automatically learned. Our method, based on Hidden-Markov Models, automatically builds a storyline of past exploration from log data events, that shows key analysis scenarios and the transitions between analysts' hypotheses and research questions. Compared to a clustering method, this approach yields higher accuracy for detecting transitions between analysis scenarios. We argue for incorporating provenance views in exploratory data analysis systems that show, at minimum, the structure and intermediate results of past exploration. Besides helping the reproducibility of the different analyses and their results, this can encourage analysts to reflect upon and ultimately adapt their exploration strategies.
Type de document :
Communication dans un congrès
Liste complète des métadonnées

Littérature citée [27 références]  Voir  Masquer  Télécharger
Contributeur : Nadia Boukhelifa Connectez-vous pour contacter le contributeur
Soumis le : mardi 28 avril 2020 - 17:03:13
Dernière modification le : mardi 13 septembre 2022 - 14:14:35


Fichiers produits par l'(les) auteur(s)



Antoine Barczewski, Anastasia Bezerianos, Nadia Boukhelifa. How Domain Experts Structure Their Exploratory Data Analysis: Towards a Machine-Learned Storyline. 2020 CHI Conference on Human Factors in Computing Systems Extended Abstracts, Apr 2020, Honolulu, Hawaii, United States. ⟨10.1145/3334480.3382845⟩. ⟨hal-02557388⟩



Consultations de la notice


Téléchargements de fichiers