Katerina Batziakoudi

dblp:267/0050 · DBLP profile ↗
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2ranked-venue papers
2as first author
2since 2021 · last 2026
0009-0003-7069-9339ORCID · corroborated

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

Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021Human-computer interaction and ubiquitous computing · 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
2 papers
Visualization and visual analytics · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Visualization and visual analytics › visual encoding
bar chart
1.012026
Beyond Log Scales: Toward Cognitively Informed Bar Charts for Orders of Magnitude Values · IEEE Trans. Vis. Comput. Graph. 2026
Visualization and visual analytics
visualization design
0.912025
Lost in Magnitudes: Exploring Visualization Designs for Large Value Ranges · CHI 2025
Visualization and visual analytics
perception
0.312026
Beyond Log Scales: Toward Cognitively Informed Bar Charts for Orders of Magnitude Values · IEEE Trans. Vis. Comput. Graph. 2026

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

crowdsourced experiment · 1.0
YearPublicationVenuePosition
2026 Beyond Log Scales: Toward Cognitively Informed Bar Charts for Orders of Magnitude Values
abstract
In this work, we challenge the dominant use of logarithmic scales to communicate values spanning multiple orders of magnitude-Orders of Magnitude Values (OMVs)-to the general public. Focusing on bar charts, we incorporate cognitive insights into visualization design to better align with how humans perceive OMVs. Studies in cognitive psychology suggest that, for large numerical ranges such as millions and billions, people do not think logarithmically. Instead, they perceive numbers in a piecewise linear manner, grouping values into scale words (e.g., millions) and applying linear reasoning within each group. We build upon a recently introduced piecewise linear scale, EplusM, and validate its use in bar charts, which we refer to as EplusM bar charts. We also introduce two novel variants of the EplusM bar chart informed by findings in numerical perception: Bricks, which builds on the concepts of round numbers and subitizing, and Multi-Magnitude, which leverages categorical perception of large numbers. In a crowdsourced experiment, we evaluate four bar chart designs: 1) Log, 2) EplusM, 3) Bricks, and 4) Multi-Magnitude, across value retrieval and quantitative comparison tasks. Our results show that EplusM bar charts are significantly preferred over logarithmic designs, increase user confidence, and reduce perceived mental demand, while maintaining task performance. These findings suggest that EplusM bar charts can serve as effective alternatives to logarithmic ones when visualizing OMVs for general audiences.
Katerina Batziakoudi, Stéphanie Rey, Jean-Daniel Fekete
IEEE Trans. Vis. Comput. Graph.1
2025 Lost in Magnitudes: Exploring Visualization Designs for Large Value Ranges
abstract
Best Paper Award
Katerina Batziakoudi, Florent Cabric, Stéphanie Rey, Jean-Daniel Fekete
CHI1