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Giovani Valdrighi

dblp:341/1469 · DBLP profile ↗
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2ranked-venue papers
2as first author
2since 2021 · last 2025
0000-0003-0106-4789ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer graphics and multimedia
1 paper
Visualization and visual analytics · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visualization and visual analytics
spatiotemporal visualization
0.812024
MoReVis: A Visual Summary for Spatiotemporal Moving Regions · IEEE Trans. Vis. Comput. Graph. 2024
Visualization and visual analytics › graph visualization
layout optimization
0.212024
MoReVis: A Visual Summary for Spatiotemporal Moving Regions · IEEE Trans. Vis. Comput. Graph. 2024

Methods — techniques the papers use, named apart from their topics

user study · 0.8layout optimization · 0.81d projection · 0.8
YearPublicationVenuePosition
2025 Best practices for responsible machine learning in credit scoring
Giovani Valdrighi, Athyrson M. Ribeiro, Jansen Silva de Brito Pereira, Vitória Guardieiro, Arthur Hendricks, Décio Miranda Filho, Juan David Nieto Garcia, Felipe F. Bocca, Thalita B. Veronese, Lucas Francisco Wanner, Marcos M. Raimundo
Neural Comput. Appl.1
2024 MoReVis: A Visual Summary for Spatiotemporal Moving Regions
abstract
Spatial and temporal interactions are central and fundamental in many activities in our world. A common problem faced when visualizing this type of data is how to provide an overview that helps users navigate efficiently. Traditional approaches use coordinated views or 3D metaphors like the Space-time cube to tackle this problem. However, they suffer from overplotting and often lack spatial context, hindering data exploration. More recent techniques, such as MotionRugs, propose compact temporal summaries based on 1D projection. While powerful, these techniques do not support the situation for which the spatial extent of the objects and their intersections is relevant, such as the analysis of surveillance videos or tracking weather storms. In this article, we propose MoReVis, a visual overview of spatiotemporal data that considers the objects' spatial extent and strives to show spatial interactions among these objects by displaying spatial intersections. Like previous techniques, our method involves projecting the spatial coordinates to 1D to produce compact summaries. However, our solution's core consists of performing a layout optimization step that sets the size and positions of the visual marks on the summary to resemble the actual values on the original space. We also provide multiple interactive mechanisms to make interpreting the results more straightforward for the user. We perform an extensive experimental evaluation and usage scenarios. Moreover, we evaluated the usefulness of MoReVis in a study with 9 participants. The results point out the effectiveness and suitability of our method in representing different datasets compared to traditional techniques.
Giovani Valdrighi, Nivan Ferreira, Jorge Poco
IEEE Trans. Vis. Comput. Graph.1