Logo des Repositoriums
 

AIDA-Vis – Automatic Data Visualization with Human Preferences

dc.contributor.authorLaurito,Walter
dc.contributor.authorHöllig,Jacqueline
dc.contributor.authorLachowitzer,Jonas
dc.contributor.authorThoma,Steffen
dc.contributor.authorBudde,Matthias
dc.contributor.authorPhilipp,Patrick
dc.contributor.editorDemmler, Daniel
dc.contributor.editorKrupka, Daniel
dc.contributor.editorFederrath, Hannes
dc.date.accessioned2022-09-28T17:10:21Z
dc.date.available2022-09-28T17:10:21Z
dc.date.issued2022
dc.description.abstractData visualization is a complex task that typically requires human expertise, acquired through a large number of professional working hours. The automatic generation of reasonable visualizations would be a good solution for inexperienced laypeople. However, existing approaches fall short since they are quite static and rely only on traditional supervised learning. This results in models which recommend a single visualization solely based on the dataset features. User preferences and goals are not taken into account. We propose a more flexible solution that is iteratively updated with the individual user's preferences and outputs a ranked list of visualizations for a given dataset.en
dc.identifier.doi10.18420/inf2022_27
dc.identifier.isbn978-3-88579-720-3
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/39525
dc.language.isoen
dc.publisherGesellschaft für Informatik, Bonn
dc.relation.ispartofINFORMATIK 2022
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-326
dc.subjectVisualization Recommendation
dc.subjectAutomated Visualization Design
dc.subjectMachine Learning
dc.subjectHuman Preferences
dc.subjectReinforcement Learning
dc.subjectEvolutionary Algorithm
dc.subjectReward Learning
dc.titleAIDA-Vis – Automatic Data Visualization with Human Preferencesen
gi.citation.endPage305
gi.citation.startPage301
gi.conference.date26.-30. September 2022
gi.conference.locationHamburg
gi.conference.sessiontitleKünstliche Intelligenz für kleine und mittlere Unternehmen (KI-KMU 2022)

Dateien

Originalbündel
1 - 1 von 1
Vorschaubild nicht verfügbar
Name:
kikmu_05.pdf
Größe:
200.67 KB
Format:
Adobe Portable Document Format