RESEARCH CENTRE

Inria Saclay Centre
at Université Paris-Saclay

IN PARTNERSHIP WITH:

CNRS, Université Paris-Saclay

2023
ACTIVITY REPORT

Project-Team

AVIZ

Analysis and VIsualiZation

IN COLLABORATION WITH: Laboratoire Interdisciplinaire des Sciences du
Numérique

DOMAIN

Perception, Cognition and Interaction

THEME

Interaction and visualization

Contents

Project-Team AVIZ

1 Team members, visitors, external collaborators

2 Overall objectives

2.1 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Research Themes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3 Research program

3.1 Progressive Data Analysis and Scalability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Physicality in Input and Output . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Research Axis 3: Perception, Cognition and Decision-Making . . . . . . . . . . . . . . . . . .
3.4 Research Axis 4: Methodology for Visualization Research . . . . . . . . . . . . . . . . . . . .

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4 Application domains
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4.1 Natural Sciences
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4.2 Social Sciences
4.3 Medicine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5 Social and environmental responsibility

10
5.1 Impact of research results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

6 Highlights of the year

11
6.1 Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
6.2 Awards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

7 New software, platforms, open data

11
7.1 New software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
7.1.1 Cartolabe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
7.1.2 PAOHvis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
7.1.3 ParcoursVis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
7.1.4 LineageD+ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
7.2 New platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
7.3 Open data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
7.3.1 Vispubdata.org . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
7.3.2 Gender in Visualization Dataset
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
7.3.3 Visualization Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
7.3.4 Multidimensional Projections of Large Corpora on Zenodo . . . . . . . . . . . . . . . 14
7.3.5 Atlas of Minoiry Languages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

8 New results

14
8.1 Visualization of Socio-Demographic Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
8.2 Design Characterization for Black-and-White Textures in Visualization . . . . . . . . . . . . 15
8.3 Visualizing and Comparing Machine Learning Predictions to Improve Human-AI Teaming

on the Example of Cell Lineage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

9 Bilateral contracts and grants with industry

16
9.1 Bilateral contracts with industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

10 Partnerships and cooperations

10.1 International research visitors

16
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
10.1.1 Visits of international scientists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
10.1.2 Visits to international teams
10.2 National initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

11 Dissemination

18
11.1 Promoting scientific activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
11.1.1 Scientific events: organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
11.1.2 Scientific events: selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
11.1.3 Journal
11.1.4 Invited talks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
11.1.5 Leadership within the scientific community . . . . . . . . . . . . . . . . . . . . . . . . 20
11.1.6 Scientific expertise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
11.2 Teaching - Supervision - Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
11.2.1 Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
11.2.2 Supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

12 Scientific production

21
12.1 Major publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
12.2 Publications of the year
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
12.3 Cited publications

Project AVIZ

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Project-Team AVIZ

Creation of the Project-Team: 2008 January 01

Keywords

Computer sciences and digital sciences

A2.1.10. – Domain-specific languages

A3.1.4. – Uncertain data

A3.1.7. – Open data

A3.1.8. – Big data (production, storage, transfer)

A3.3. – Data and knowledge analysis

A3.3.1. – On-line analytical processing

A3.3.3. – Big data analysis

A3.5.1. – Analysis of large graphs

A5.1. – Human-Computer Interaction

A5.1.1. – Engineering of interactive systems

A5.1.2. – Evaluation of interactive systems

A5.1.6. – Tangible interfaces

A5.1.8. – 3D User Interfaces

A5.1.9. – User and perceptual studies

A5.2. – Data visualization

A5.6.1. – Virtual reality

A5.6.2. – Augmented reality

A6.3.3. – Data processing

A9.6. – Decision support

Other research topics and application domains

B1. – Life sciences

B1.1. – Biology

B1.2. – Neuroscience and cognitive science

B9.5.6. – Data science

B9.6. – Humanities

B9.6.1. – Psychology

B9.6.3. – Economy, Finance

B9.6.6. – Archeology, History

B9.6.10. – Digital humanities

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Inria Annual Report 2023

1 Team members, visitors, external collaborators

Research Scientists

• Jean Daniel Fekete [Team leader, INRIA, Senior Researcher, HDR]

• Tobias Isenberg [INRIA, Senior Researcher, HDR]

• Petra Isenberg [INRIA, Senior Researcher, HDR]

Faculty Member

• Frederic Vernier [UNIV PARIS SACLAY, Associate Professor]

Post-Doctoral Fellows

• Florent Cabric [INRIA, Post-Doctoral Fellow]

• Alexis Pister [University of Edinburgh, from Mar 2023 until Sep 2023]

• Mickael Sereno [INRIA, Post-Doctoral Fellow]

PhD Students

• Katerina Batziakoudi [BERGER-LEVRAULT, CIFRE, from Feb 2023]

• Tingying He [UNIV PARIS SACLAY]

• Jiayi Hong [INRIA, until Mar 2023]

• Mohammad Alaul Islam [INRIA, until Mar 2023]

• Yucheng Lu [UNIV PARIS SACLAY, from Nov 2023]

• Lijie Yao [INRIA]

Technical Staff

• Ludovic David [INRIA, Engineer]

• Olivier Gladin [INRIA, Engineer]

• Hande Gozukan [INRIA, Engineer]

• Christian Poli [INRIA, Engineer]

Interns and Apprentices

• Anne-Flore Cabouat [UNIV PARIS SACLAY, Intern, from Oct 2023]

• Anne-Flore Cabouat [INRIA, Intern, from Mar 2023 until Aug 2023]

• Yucheng Lu [UNIV PARIS SACLAY, Intern, from Mar 2023 until Aug 2023]

• Lisa Taldir [Université Paris-Saclay, Intern, from May 2023 until Jul 2023]

Administrative Assistant

• Katia Evrat [INRIA]

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Visiting Scientists

• Fairouz Grioui [Univ Stuttgart, until Mar 2023]

• Johannes Knittel [University of Stuttgart, until Jan 2023]

• Gabriela Carolina Molina Leon [Univ Bremen, from May 2023 until Jul 2023]

• Hendrik Strobelt [IBM Research, until Jan 2023]

• Junxiu Tang [Univ Zhejiang, from Nov 2023]

• Karelia Alexandra Vilca Salinas [UNIV SAO PAULO, from Apr 2023]

External Collaborator

• Gabriela Carolina Molina Leon [Univ Bremen, from Aug 2023]

2 Overall objectives

2.1 Objectives

Aviz (Analysis and VIsualiZation) is a multidisciplinary project that seeks to improve data exploration
methods, techniques, and tools based on Interactive Visualization. Visualization, in general, refers to the
graphical representation of data or concepts to aid access, distribution or explanations of data. Card et al.
give a general definition for visualization as

“the use of computer-supported, interactive, visual representations of data to amplify

cognition.” [42]

Taking this definition, visualization is a means of creating visual aids that lead to insight in the
underlying data sets. It is not about producing nice pictures but about making data understandable
and explorable so that visualizations help viewers gain knowledge about the data. It is about aiding
the process of forming a mental model for the acquired data and so helping the viewer to understand
underlying concepts, patterns, and connections within the data [68]. In partiular, visualization has the
goal to improve humans’ sensemaking of complex data by taking advantage of the capabilities of their
vision system: visual information can be processed in parallel and with a high bandwidth into the human
cognitive centers [75]. Ware defines five advantages of visualization [75]:

1. Comprehension: Supports the comprehension of large amounts of data.

2. Pattern Perception: Previously unnoticed properties of data may emerge.

3. Problem Analysis: Problems within the data may become immediately apparent.

4. Adaptability: facilitates understanding of large- and small-scale features of data.

5. Interpretation: Hypothesis formulation is facilitated.

Three main areas of visualization have evolved in the computer science community: Scientific Visual-
ization, Information Visualization, and Visual Analytics. Scientific visualization is primarily concerned
with displaying real or simulated scientific data. Basic visualization techniques for this area include
surface rendering, volume rendering, and animation. Typical examples include processing of satellite
photographs, fluid flows, or medical data. Datasets in information visualization typically come from
large information spaces or information systems and are both structured or unstructured. Examples
include network data, multi-dimensional tables of abstract measurements, or unstructured data such as
text. Visual analytics, finally, is concerned with augmenting human-led data exploration with automatic
techniques such as machine learning. The Aviz team has expertise in all three areas of visualization.

As shown in Figure 1, visualization deals with the data analysis pipeline and research in visualization
has been addressing all the stages with less emphasis on the two initial ones and the last one. In its

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Inria Annual Report 2023

Figure 1: The conceptual Data Analysis Pipeline related with the four themes of AVIZ.

initial incarnation, Aviz has been focusing on interaction in combination with visualization, physical
presentation, and perception. We now want to expand our research to wider questions, both in the
human-side and in the system side. For the human side, we want to better understand human perception
and cognition to improve the visualization techniques, so as to better convey information to the human
brain. We also want to better understand the human biases to overcome them when possible, or provide
methods to avoid them otherwise.

On the system side, we want to expand the scope of visualization that is currently limited to relatively
small datasets and relatively simple analytical methods. To achieve scalability in visualization, we will
focus on a paradigm shift: progressive data analysis. Long-running computations currently hamper the
exploration and visualization process because human’s attention is limited by latency constraints. We
want to design exploratory systems that provide continuous feedback and allow interactions at any time
during computation. The new progressive data analysis paradigm offers these capabilities, but to be
usable, it requires the whole analytical pipeline to be re-implemented, and visualization and interaction
techniques to be adapted.

2.2 Research Themes

Aviz’s research on Visualization and Visual Analytics is organized around four research themes, described
in more detail in the next section. Instead of addressing point problems, each research theme will address
several stages of the visualization pipeline in a holistic manner, as summarized in Figure 1.

1. Progressive Data Analysis and Scalability will address visualization scalability problems. Existing
data analysis systems (such as Tableau [69], R [64], or Python with its data analysis ecosystem [56]) are
not scalable for exploratory analysis because their latency is not controllable. This theme will lay out the
foundations of progressive data analysis systems, which generate estimates of the results and updates the
analyst continuously at a bounded pace. It will focus on all the stages of the data analysis pipeline: data
management mechanisms, data analysis modules, as well as visualizations, perception, understanding,
and decision making.

2. Physicality in Input and Output will seek to better understand the benefits of physicality for
information. Although beyond-desktop environments for visualization are generating more and more
interest, theories and empirical data are lacking. This theme will consolidate the nascent areas of data
physicalization, situated visualization, and immersive visualization.

3. Perception, Cognition, and Decision Making will study how we perceive and understand visualiz-
ations in order to develop generalized guidelines for optimizing effectiveness. It will generalize results
obtained with simple charts to more complex visualizations of large datasets, establish theories on the
use of abstraction in visualization, and contribute new empirical knowledge on decision making with
visualizations.

4. Methodologies for Visualization Research will develop new methods to ground the study of the
above scientific questions, and to benefit visualization more generally. This theme will develop evidence-
based strategies for communicating quantitative empirical findings, and will promote methodological
discussions and open research practices within the field.

Project AVIZ

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3 Research program

3.1 Progressive Data Analysis and Scalability

Permanent involved: Jean-Daniel Fekete

While data analysis has made tremendous progress in scalability in the last decade, this progress
has only benefited “confirmatory” analysis or model-based computation; progress in data exploration
has lagged behind. Existing data analysis systems do not support data exploration at scale because, for
large amounts of data or for expensive computations, their latency is not controllable: computations can
take minutes, hours, even days and months. Cognitive psychologists have shown that humans’ cognitive
capabilities degrade when latency increases [62, 67]. Miller [62] points out that the feedback of a system
should remain below 10 seconds to maintain the user’s attention. Therefore, to try to limit the latency,
analysts currently resort to complex, inefficient, and unsatisfactory strategies, such as sampling with its
issues related to representativity.

To address the scalability problem under controlled latency, instead of performing each computation
in one long step that forces the analyst to wait for an unbounded amount of time, a progressive system
generates estimates of the results and updates the analyst continuously at a bounded pace. The process
continues until the computation is complete, or it stops early if the analyst considers that the quality of
the estimates is sufficient to make a decision. During the process, a progressive system allows users to
monitor the computation with visualizations and steer it with interactions.

While the topic of progressive data analysis has started to emerge in the late 90’s, it has remained
marginal practically because it touches three fields of computer science that are traditionally separate:
data management, data analysis, and visualization. Research on progressive data analysis remains
fragmented; the solutions proposed are partial and the different solutions cannot always be combined.
We have organized a Dagstuhl seminar on Progressive Data Analysis and Visualization [46, 70] that
acknowledged the harm of this topical separation and devised a research agenda. Aviz will participate in
this agenda with specific assets.

Aviz is actively working on designing and implementing the ProgressiVis language that is natively
progressive [47]. The language relies on a Python interpreter but its execution semantics is different in the
sense that all the operations that would take time to execute are performed progressively. The ProgressiVis
system touches all the stages of the conceptual data analysis pipeline of Figure 1; it integrates data
management mechanisms, data analysis modules, as well as visualizations, perception, understanding,
and decision making. Aviz will strengthen its work on the implementation of a natively progressive data
science system. Such a system will lead to the following research topics:

1. Progressive language kernel and data management mechanisms

2. Progressive algorithms and computation strategies

3. Progressive visualizations

4. Management of uncertainties, computed from the algorithms and conveyed to the analysts.

Language: ProgressiVis relies on a Python interpreter that is convenient for quick prototyping. We are
using many internal mechanisms of the Python language to create a toolkit that allows programming
progressive applications in a convenient way.

Algorithms: Existing libraries implementing algorithms are either “eager”, sometimes “online”, and
more rarely “streaming”, but very few are compatible with our progressive paradigm. We already designed
several progressive algorithms and want to further expand the range of algorithms we can support, and
better understand how algorithms could be transformed into their progressive counterpart.

Progressive computation can trade speed, memory, and quality. When analysts want to compute
rough estimates of their results quickly, several progressive algorithms offer parameters to control the
tradeoffs. For example, our progressive k-nearest-neighbors algorithm [54] allows choosing the number of
k-d-trees that influences the memory footprint, accuracy, and speed. Progressive data analysis also uses
and adapts streaming algorithms, also called “data sketches” [48], that offer various tradeoffs between
speed, memory, and quality.

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Inria Annual Report 2023

Figure 2: Examples of progressive bar charts we studied. All the bar charts represent the same data at
one step of the progression. For our proposed new designs (c) and (d), opacity and position along the
X-axis encode the recency of the updates; opaque bars on the right represent more recent updates, and
transparent bars on the left represent older updates.

Visualizations: Like algorithms need to be adapted or transformed, visualization techniques and user
interfaces also need to be adapted to be used in a progressive setting.

For the visualization pipeline, including the rendering phase, there has been previous work related to
progressive layout computation and progressive rendering. For example, in network visualization, there
is an old tradition of using iterative force-directed solvers and showing their results progressively when
computing complex graph layouts. We generalize this approach to all the visualizations. Progressive
rendering is popular in the real-time computer graphics and gaming domains, but new in visualization.
We are designing visualization techniques based on GPU and accelerated algorithms to better render
aggregated visualizations, such as heatmaps and sampled visualizations.

We have also started to explore the problem of requirements for progressive user interfaces [38],
leading the community of researchers to understand that progressive systems need to provide effective
assessments of the quality of their results to allow analysts to make early decisions. This justifies the next
research topic on the management of uncertainties. We have also started to propose new data models for
managing aggregated visualizations [55]. We now continue our research to adapt existing visualization
techniques and interactive environments to deal with progressive results and parameter steering.

Management of uncertainties: When performing exploratory data analysis on large data, analysts start
by trying some algorithms with different parameters until they obtain useful results. Therefore, it is of
paramount importance to be able to realize that a specific algorithm or a specific set of parameters is
not leading to a useful result and to abort an analysis before it ends to explore other methods. On the
other side, in many circumstances when data is “well-behaved”, progressive algorithms achieve very
good results quickly, and analysts can make a decision before the end of the algorithms’ execution. The
assessment of a quality measure related to a computed result is a complex problem, already tackled by
other communities (e.g., streaming, approximate queries).

Progressive Data Analysis adds two problems: (1) controlling uncertainty during progressive com-
putations, and (2) conveying the results of this uncertainty in a way understandable by analysts, while
avoiding possible cognitive biases such as priming and anchoring [57].

To control the uncertainty during the progression of a computation, methods related to Sequential
Analysis [49] should be used. They are well-known in the area of clinical trials and have evolved recently
to manage large amounts of data while controlling time or quality in Approximate Query Databases such
as BlinkDB [37].

We have already shown that humans are quite good at deciding when to stop a progressive process
to make an early decision using progressive bar charts [5] (Figure 2); humans are cognitively equipped
to make sense of progressive processes. Additionally, visualization can help humans make sense of and
interact with progressive processes, such as our new-history bar chart designs that help to render visually
when a progressive process starts to converge, opening a new line of research to discover the possible
forms of support.

3.2 Physicality in Input and Output

Permanents involved: Petra Isenberg, Tobias Isenberg, Jean-Daniel Fekete

Project AVIZ

7

Figure 3: Example results from our work on Axis 2 for each of our focus areas.

During the last five years, we expanded our work on beyond-desktop environments for visualization.
Our team has made contributions in the areas of data physicalization, visualization for wearable devices,
situated and embedded visualization, and visualization in augmented reality.

Data Physicalization: Data physicalization is a rich and vast research area that studies the use of phys-
ical artifacts to convey data. It overlaps with a number of research areas including information/scientific
visualization, visual analytics, tangible user interfaces, shape-changing interfaces, fabrication, as well
as graphic design, architecture, and art. Physical data visualizations tap into our lifelong experience of
perceiving and manipulating the physical world, either alone or with other people. Among the earliest
man-made artifacts are physical representations of semantic concepts that provide physical metaphors
that allow us to reason, remember, and communicate. With the advent of computers, we have substi-
tuted physical representations with pixels on a computer screen. The resurgence of physicalization as a
research area, following our early definitions [53], asks what we have lost in this transformation. Certainly,
a computer-based visualization allows us to zoom an image, transform variables in real time, and to
zoom through virtual computer-based world. However, these representations can sever the relationship
to the natural world, depriving us of the touch, feel, and emotion that comes from interacting with real
objects. We studied several aspects of data physicalization including technical challenges of constructing
physicalizations, potential benefits, and how historical examples could transfer to a modern world.

Visualization for Wearable and Mobile Devices: In the area of wearable and mobile-devices we
engaged in device-driven research where we considered how small device form factors may influence
how we need to design visualizations and how we can use them. Portable and wearable personal devices
such as fitness tracking armbands, hand-held GPS trackers, smart watches, or mobile phones are very
small displays that are capable of producing data themselves (through sensors), downloading it from other
sources (through Wifi or Bluetooth), and displaying it immediately [4]. Often the data is shown in the form
of visualizations that have to be adapted to the small display size. We consider very small visualizations
that are often used on such devices under the term “micro visualization” and have been working towards
a better understanding of the complexities involved in designing and using micro visualizations but also
studied the influence of the unique context of use of mobile devices on visualization use and design.

Situated and Embedded Data Representations: We study how embedding data visualizations in
the context of the data sources can empower people to make effective use of their data in a variety of
application contexts. Our goal in this work is to go beyond the traditional platforms of data analytics
by using situated data visualizations on various types of non-traditional displays. In a situated data
visualization, the data is directly visualized near the physical space, object, or person it originates from
[76]. For example, a person may attach small e-ink displays embedded with sensors at various locations
of their house or their workplace, to better understand their use of space, of equipment, or of energy
resources. Or a person who wishes to exercise more may use an augmented reality device to visualize their
past running performance in-place. New situated data visualizations like these can surface information
in the environment—allowing viewers to interpret data in-context and take action in response to it [8].

Visualization using Augmented Reality Devices: Many datasets are 3D-spatial in nature and re-
searchers and practitioners could benefit from seeing them in true 3D space. This is where immersive
technologies shine, and the recent advances in VR and AR headset technologies have made such dis-
plays accessible to the general public—the lack of large dedicated VR installations such as a CAVE is not
preventing the use of immersive rendering anymore. Nonetheless, the investigation of 3D datasets also
frequently requires researchers to use tools such as scripted analysis and statistical evaluation, and such
direction of investigation will continue to be a cornerstone of scientific work. In our investigations we are
thus interested in looking at, in particular, hybrid setups that allow researchers to use the best of both

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Inria Annual Report 2023

worlds: traditional workstations combined with an AR overlay for stereoscopic rendering of 3D data [74].

3.3 Research Axis 3: Perception, Cognition and Decision-Making

Permanents involved: Petra Isenberg, Tobias Isenberg, Jean-Daniel Fekete

As we collect increasingly large amounts of data in fields such as climate science, finance, and medi-
cine, the need to understand and communicate that data becomes more important. Data visualizations
are often used to give an overview of information, however it can be challenging to predict whether these
visualizations will be effective before spending resources to develop them. Consequently, researchers
make use of experimental methods from visual perception and cognition to study how we perceive and
understand visualizations in order to develop generalized guidelines for optimizing effectiveness. On this
research axis we have three focus areas:

Perception of Visualizations in Novel Contexts. Novel technology and usage contexts have several
characteristics for data visualizations that warrant a (re-)evaluation of how well people can perceive
visualizations. Characteristics we were particularly interested in include:

• Physical factors: Many now common displays have characteristics that warrant (re-)evaluation
of what we know about visualizations to be displayed on them. Small form factors of displays
such as smartwatches and fitness bands are an obvious characteristic. Some screens deviate from
the standard rectangular form we are familiar with and use a circular geometry, which is another
interesting design constraint for visualization.

• Data display mobility: Data display mobility captures the movement of the display(s) containing
visual representations of data. Fixed, movable, carryable, wearable, and independently moving
displays can be differentiated along this dimension. We conducted some of our research on
carryable devices, including mobile devices such as smartphones and tablets but also wearable
displays such as smartwatches and head-mounted displays.

• Context-of-use: Many novel displays are used in contexts that are much unlike traditional office
settings. Visualizations here may be subjected to different lighting conditions and viewers may
only afford very quick glances at the displays themselves. For example, when in a car the driver can
only afford very quick glances at the GPS before returning to the primary task of arriving safely.

Within this focus area we worked on some of these aspects; such as the need for quick glances, the display
size of visualizations, the movement of viewers or the data, and the understanding of 3D augmented reality
spaces. We relied mostly on mixed-methods user studies where a quantitative analysis methodology was
coupled with interviews or questionnaires.

Illustrative Visualization. This focus area takes inspiration from illustrators’ decades to centuries of
experience on perception and cognition to better portray scientific subject matter. Another input arises
from the field of non-photorealistic rendering which has developed numerous techniques of stylizing
images and other input data. Traditionally, illustrative visualization has thus been applied primarily to
data with a concrete spatial mapping in 2D and, more frequently, in 3D space.

Another main direction of research in this context is what the role of abstraction is in illustrative
visualization [73, 72] as well as visualization in general, and specifically how we can provide dedicated
means to control the abstraction being applied to visual representations of data. This means that we
need to go beyond seeing abstraction only as a side-product of stylization as it has traditionally been
viewed in many approaches in non-photorealistic rendering as well as illustrative visualization to date,
and investigate how we can interactively adjust it to provide practitioners with a means to find the best
visual representations for a given task. For example, we have investigated this question in the context of
structural biology [79] and DNA nanostructures [59, 61, 60], but also want to expand this work to other
application domains in the future.

Decision-Making with Visualizations. Human decision-making and cognitive biases are important
research topics in the fields of psychology, economics and marketing. Visualization systems are increas-
ingly used to support decision-making: large companies switch to visualization solutions to improve
their decisions in a range of areas, where large sums of money or human lives are at stake. More and

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more, the ultimate goal of visualization is not to understand patterns in the data and get insights as
was traditionally assumed, but to make good decisions. In order to fully understand how information
visualizations can support decision-making, it is important to go beyond traditional evaluations based
on data understanding, and study how visualizations interact with human judgment, human heuristics,
and cognitive biases.

We pursue this important stream of research by investigating decision-making in the presence of
uncertainty and incomplete information (in connection with the topics discussed in subsection 3.1 and
the use of visualizations to support social choice and group decisions in the presence of conflicts of
interest. How cognitive biases interact with visual perception is also an important and difficult question
that has remained largely unexplored.

3.4 Research Axis 4: Methodology for Visualization Research

Permanents involved: Petra Isenberg, Tobias Isenberg, Jean-Daniel Fekete

An important aspect of any scientific research is to establish and follow rigorous and effective meth-
odologies for acquiring new knowledge. In the field of Visualization in particular, scientific discourse on
the validity, use, and establishment of methodologies is important as the field is highly interdisciplinary,
with diverse influences and opinions. It is important to establish, for example, what level of rigor the field
should require of its methods, how to choose among established methods and methodologies, and how
to best communicate the results of our empirical research. We focus our efforts on three main topics
related to visualization research methodologies.

Promoting and Following Open Research Practices

For issues with open research practices to be addressed, educational materials and guidelines need to
be written, so researchers have clarity about how to make their research more credible. Aviz members
are working with the organizing bodies of the visualization research community to establish incentives
for making research artifacts and potentially establish minimal requirements for openness in published
articles. Meanwhile, it is important to continue measuring and cataloging openness in the field to monitor
progress. The goal is to improve the credibility and applicability of the field’s research.

Shaping the Scientific Visualization Community Aviz researchers are heavily involved in the organ-
ization structure of IEEE visualization conferences, the most prestigious conference in our field, by
proposing workshops, tutorials, serving on various organizing committees, steering committees, editorial
boards. We, in particular, aid the process by providing data collection and analysis services through
the vispubdata.org dataset that we are collecting, cleaning, and making available to the community.
The dataset has already been used in research (e. g., [58]) but also to shape the scientific community by
proposing program committee members, new processes, and was used by the Visualization Restructuring
Committee (ReVISe). We are also involved in the EuroVis community and participate at multiple levels to
its organization and management.

4 Application domains

4.1 Natural Sciences

As part of a CORDI PhD project, we collaborated with researchers at CERN on interactive data visualization
using augmented reality, with the goal to better understand this new visualization environment and
to support the physicists in analysing their 3D particle collision data. As part of another CORDI PhD
project, we collaborated with researchers at the German Center for Climate Computation (DKRZ) and
the Helmholtz-Zentrum Hereon (formerly Helmholtz-Zentrum Geesthacht, HZG), to better understand
collaborative data exploration and interaction in immersive analytics contexts. Finally, as part of the Inria
IPL “Naviscope,” we collaborate with researchers at INRAE (as well as other Inria teams) on interactive
visualization tools for the exploration of plant embryo development. We also work with experts from the
natural sciences in general to create illustrative visualizations of scientific data, such as a continuous
zooming technique from the nucleus of a cell all the way down to the atom configuration of the DNA, for
example for the application in education.

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4.2 Social Sciences

We collaborate with social science researchers from EHESS Paris on the visualization of dynamic networks;
they use our systems (GeneaQuilts [39], Vistorian [66], PAOHVis [71], PK-Clustering [63]) and teach them
to students and researchers. Our tools are used daily by ethnographers and historians to study the
evolution of social relations over time. In the social sciences, many datasets are gathered by individual
researchers to answer a specific question, and automated analytical methods cannot be applied to these
small datasets. Furthermore, the studies are often focused on specific persons or organizations and
not always on the modeling or prediction of the behavior of large populations. The tools we design to
visualize complex multivariate dynamic networks are unique and suited to typical research questions
shared by a large number of researchers. This line of research is supported by the DataIA “HistorIA”
project, and by the “IVAN” European project. We currently collaborate with PayAnalytics, an Icelandic
company to visualize data to help companies close their gender pay gaps.

4.3 Medicine

We collaborate with the Health-Data-Hub on the analysis and visualization of CNAM Data “parcours
de santé” to help referent doctors and epidemiologists make sense of French health data. In particular,
we are working on a subset of the CNAM Data focused on urinary problems, and we have received very
positive feedback from doctors who can see what happens to the patients treated in France vs. what they
thought happened through the literature. This project is starting but is already getting a lot of traction
from our partners in medicine, epidemiology, and economy of health.

We are also collaborating with the “Assistance Publique - Hôpitaux de Paris” AP-HP with a newly
funded project called URGE, aimed at improving the emergency services for the parisian hospitals. See
the press announcement.

5 Social and environmental responsibility

5.1 Impact of research results

Aviz’ work on illustrative visualization has the potential to be integrated into future teaching materials for
students in schools, visitors in museums, or similar.

Aviz’ work on visualization of large documents corpora with Cartolabe is used to present the results of

the French “Grand débat”, as well as other citizen expressions.

Aviz’ work on the gender pay gap aims at improving decision making for closing the adjusted pay gap.
Open science: Aviz regularly shares full research material on the repository of the Center for Open

Science to facilitate scrutiny, reuse, and replication:

• Visualization in Motion: A Research Agenda and Two Evaluations [78] — osf.io/km3s2/, osf.io/9c4bz/,

osf.io/t748d/, osf.io/t748d/,gitlab.inria.fr/lyao/visinmotion, GRSI Stamp

• Reflections on Visualization in Motion for Fitness Trackers [52] — hal.inria.fr/hal-03775633

• Situated Visualization in Motion for Swimming [77] — hal.inria.fr/hal-03700406

• Situated Visualization in Motion for Video Game [40] — hal.inria.fr/hal-03694019

• Situated Visualization in Motion in Video Game for Different Types of Data [41] — hal.inria.fr/hal-

03700418

• Preferences and Effectiveness of Sleep Data Visualizations for Smartwatches and Fitness Bands [50]

– osf.io/yz8ar/

• Context Specific Visualizations on Smartwatches [51] – osf.io/vhn43/

• Studying Early Decision Making with Progressive Bar Charts [5] — osf.io/tn2ph/

• Scalability in Visualization [65] — osf.io/xrvu7/

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• Six methods for transforming layered hypergraphs to apply layered graph layout algorithms [45] —

osf.io/grvwu/

• Pyramid-based Scatterplots Sampling for Progressive and Streaming Data Visualization [44] —
ProgressivePyramidBasedSampling and www.yunhaiwang.net/Vis2021/progressive-sampling/

Graphics Replicability Stamp: In 2023, Aviz members received recogniztion for the replicability of

their work from the Graphics Replicability Stamp Initiative for several of their journal publications:

• Designing for Visualization in Motion: Embedding Visualizations in Swimming Videos [24]

• Eliciting Multimodal and Collaborative Interactions for Data Exploration on Large Vertical Displays

[19]

• Design Characterization for Black-and-White Textures in Visualization [13]

• Visualizing and Comparing Machine Learning Predictions to Improve Human-AI Teaming on the

Example of Cell Lineage [15]

• V-Mail: 3D-Enabled Correspondence about Spatial Data on (Almost) All Your Devices [20]

• BeauVis: A Validated Scale for Measuring the Aesthetic Pleasure of Visual Representations [12]

6 Highlights of the year

6.1 Events

• Florent Cabric, post-doctoral fellow at Aviz co-organized the French “Visualization Day” (“Journées

Visu”) at Univ. Paris-Saclay in June.

• Petra Isenberg, Florent Cabric, and Mickaël Sereno co-organized (“PreVis”) at Univ. Paris-Saclay in

June.

6.2 Awards

• Tobias Isenberg: 14 Years SciVis Test-of-Time Award at IEEE VIS 2023

• Petra Isenberg: Induction into the IEEE Visualization Academy

7 New software, platforms, open data

7.1 New software

7.1.1 Cartolabe

Name: Cartolabe

Keyword: Information visualization

Functional Description: The goal of Cartolabe is to build a visual map representing the scientific activity
of an institution/university/domain from published articles and reports. Using the HAL Database,
Cartolabe provides the user with a map of the thematics, authors, and articles. ML techniques are
used for dimensionality reduction, cluster, and topic identification, visualization techniques are
used for a scalable 2D representation of the results.

Cartolabe has, in particular, been applied to the Grand Debat dataset (3M individual propositions
from French Citizen, see https://cartolabe.fr/map/debat). The results were used to test both the
scaling capabilities of Cartolabe and its flexibility to non-scientific and non-English corpora. We
also added sub-map capabilities to display the result of a year/lab/word filtering as an online
generated heatmap with only the filtered points to facilitate the exploration. Cartolabe has also

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Inria Annual Report 2023

been applied in 2020 to the COVID-19 Kaggle publication dataset (Cartolabe-COVID project) to
explore these publications.

URL: http://www.cartolabe.fr/

Publication: hal-02499006

Contact: Philippe Caillou

Participants: Philippe Caillou, Jean Daniel Fekete, Michèle Sebag, Anne-catherine Letournel, Hande

Gozukan

Partners: LRI - Laboratoire de Recherche en Informatique, CNRS

7.1.2 PAOHvis

Name: Parallel Aggregated Ordered Hypergraph Visualization

Keywords: Dynamic networks, Hypergraphs

Functional Description: Parallel Aggregated Ordered Hypergraph (PAOH) is a novel technique to visual-
ize dynamic hypergraphs [71]. Hypergraphs are a generalization of graphs where edges can connect
more than two vertices. Hypergraphs can be used to model co-authorship networks with multiple
authors per article, or networks of business partners. A dynamic hypergraph evolves over discrete
time slots. A PAOH display represents vertices as parallel horizontal bars and hyperedges as vertical
lines that connect two or more vertices. We believe that PAOH is the first technique with a highly
readable representation of dynamic hypergraphs without overlaps. It is easy to learn and is well
suited for medium size dynamic hypergraph networks such as those commonly generated by digital
humanities projects - our driving application domain.

URL: https://aviz.fr/paohvis

Contact: Jean Daniel Fekete

7.1.3 ParcoursVis

Name: Visualization of Patient Pathways

Keywords: Visualization, Health, Progressive visualization

Scientific Description: We developed ParcoursVis, our Progressive Visual Analytics (PVA) tool to explore
patients’ care pathways at scale. Current tools to visualize temporal event sequences are restricted
to datasets as large as a few thousand sequences to remain reactive. With ParcoursVis, we aim to
visualize patients’ care pathways stored in country-level databases, which can contain order of
magnitudes higher of event sequences, at scale using a progressive architecture. PVA tools, instead
of waiting for the whole computation to finish before rendering the final results, yield partial results
each time the algorithm processes small chunks of data or iterations. This paradigm makes the tool
reactive and quickens processes such as checking errors of a query.

Functional Description: ParcoursVis allow extracting a subset of the nationwide database from CNAMTS,
transforming the raw data into meaningful medical events, and visualizing it interactively at scale
via a web interface.

For the moment, ParcoursVis focuses on non-cancerous prostate adenoma. With this focus, our
domain expert users extract meaningful high-level types of events (e.g., treatments and outcomes)
that the patients undertake in their care pathways.

Using a progressive visualization method, ParcoursVis visualizes in an aggregated manner the care
pathways of tens of millions of patients treated with thousands of events over decades, several
orders of magnitude more than existing interactive systems.

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URL: https://www.aviz.fr/Research/ParcoursVis

Authors: Jean Daniel Fekete, Mickael Sereno

Contact: Jean Daniel Fekete

7.1.4 LineageD+

Name: LineageD+

Keywords: Data visualization, Visual analytics, Machine learning, Human-AI teaming, Biology

Functional Description: We visualize the predictions of multiple machine learning models to help
biologists as they interactively make decisions about cell lineage—the development of a (plant)
embryo from a single ovum cell. Based on a confocal microscopy dataset, traditionally biologists
manually constructed the cell lineage, starting from this observation and reasoning backward in
time to establish their inheritance. To speed up this tedious process, we make use of machine
learning (ML) models trained on a database of manually established cell lineages to assist the
biologist in cell assignment. Most biologists, however, are not familiar with ML, nor is it clear to
them which model best predicts the embryo’s development. We thus have developed a visualization
system that is designed to support biologists in exploring and comparing ML models, checking the
model predictions, detecting possible ML model mistakes, and deciding on the most likely embryo
development. To evaluate our proposed system, we deployed our interface with six biologists in an
observational study. Our results show that the visual representations of machine learning are easily
understandable, and our tool, LineageD+, could potentially increase biologists’ working efficiency
and enhance the understanding of embryos.

URL: https://github.com/JiayiHong/LineageD_Plus

Contact: Tobias Isenberg

Partner: INRAE

7.2 New platforms

Prepare the ProgressiVis Toolkit for a Wider Distribution

Participants:

Jean-Daniel Fekete (Inria, Paris-Saclay, correspondent), Chris-
tian Poli (Inria), Nivan Ferreira (Univ. Federal de Pernambuco, Brazil).

The ProgressiVis toolkit, implementing a Progressive Data Analysis and Visualization language and
environment, has been under heavy development for many years. We have finished its packaging so it
can be easily installed by partners who want to experiment with the toolkit and the concept it supports.
A documentation is also available (in progress), as well as several graphical and interactive widgets to
support its use in modern notebooks.

7.3 Open data

7.3.1 Vispubdata.org

Participants:

Petra Isenberg (Inria Paris-Saclay, correspondent), Tobias Isenberg (In-
ria Paris-Saclay).

AVIZ members are making available for research a dataset of IEEE VIS publications at vispubdata.org.

This dataset is actively being used for research and conference organization.

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Inria Annual Report 2023

7.3.2 Gender in Visualization Dataset

Participants: Natkamon Tovanich (System X), Pierre Dragicevic (Inria Bordeau),

Petra Isenberg (Inria Paris-Saclay, correspondent).

In the context of our paper on “Gender in 30 Years of IEEE Visualization” we make available data
about the diversity of the Visualization community in terms of the gender of authors, organizing, and
program committee members as well as award winners: www.aviz.fr/Research/Gender-in-Vis.

7.3.3 Visualization Images

AVIZ members are contributing to making available for research a dataset of IEEE VIS images at ieee-
dataport.org/open-access/ieee-vis-figures-and-tables-image-dataset [43]. The set of images can be
browsed online at vispubfigures.github.io/VisPubFigures/. The work is primarily led by Jian Chen at Ohio
State University.

7.3.4 Multidimensional Projections of Large Corpora on Zenodo

For the Cartolabe project, we compute multidimensional projections of large publication corpora (HAL,
arXiv, Wikipedia, etc.) as well as thematic clusters with meaningful labels. All there computed projections
are distributed as open data files on the Zenodo repository to be reused by other projects: https:
//zenodo.org/communities/cartolabe?q=&l=list&p=1&s=10&sort=newest

7.3.5 Atlas of Minoiry Languages

Frederic Vernier is part of the Speaking atlas of minority languages, a repository of audios and transcrip-
tions of the same text recorded from 920 persons over the world. https://atlas.limsi.fr/liste.h
tml

8 New results

8.1 Visualization of Socio-Demographic Data

Participants:

Florent Cabric (LISN, Université Paris-Saclay, CNRS, Inria, Saclay,
France, correspondent), Margrét VilborgBjarnadóttir[Univ. of Mary-
land] , Anne-Flore Cabouat (LISN, Université Paris-Saclay, CNRS, Inria,
Saclay, France), Petra Isenberg (LISN, Université Paris-Saclay, CNRS,
Inria, Saclay, France).

We started investigations into the visualization of sociodemographic data. Sociodemographic data is
a common part of datasets related to people, including institutional censuses, health data systems, and
human-resources files. This data is sensitive, and its collection, sharing, and analysis require careful con-
sideration. For instance, the European Union, through the General Data Protection Regulation (GDPR),
protects the collection and processing of any personal data, including sexual orientation, ethnicity, and
religion. Data visualization of sociodemographic data can reinforce stereotypes, marginalize groups,
and lead to biased decision-making. It is, therefore, critical that these visualizations are created based
on good, equitable design principles. In a first opinion paper [2], we discuss and provide a set of open
research questions around the visualization of sociodemographic data. Our work contributes to an
ongoing refection on representing data about people and highlights some important future research
directions for the Visualization community. A version of this paper and its fgures are available online at
osf.io/a2u9c.

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Figure 4: Visualization of the typical color used to represent gender in scientific publications for two
different time periods.

8.2 Design Characterization for Black-and-White Textures in Visualization

Participants:

Tingying He (Inria, Paris-Saclay, correspondent), Yuanyang Zhong (In-
ria, Paris-Saclay), Petra Isenberg (Inria, Paris-Saclay), Tobias Isen-
berg (Inria, Paris-Saclay).

We investigate the use of 2D black-and-white textures for the visualization of categorical data and
contribute a summary of texture attributes, and the results of three experiments that elicited design
strategies as well as aesthetic and effectiveness measures [3]. Black-and-white textures are useful, for
instance, as a visual channel for categorical data on low-color displays, in 2D/3D print, to achieve the
aesthetic of historic visualizations, or to retain the color hue channel for other visual mappings. We
specifically study how to use what we call geometric and iconic textures. Geometric textures use patterns
of repeated abstract geometric shapes, while iconic textures use repeated icons that may stand for data
categories. We parameterized both types of textures and developed a tool for designers to create textures
on simple charts by adjusting texture parameters. 30 visualization experts used our tool and designed 66
textured bar charts, pie charts, and maps. We then had 150 participants rate these designs for aesthetics.
Finally, with the top-rated geometric and iconic textures, our perceptual assessment experiment with 150
participants revealed that textured charts perform about equally well as non-textured charts, and that
there are some differences depending on the type of chart.

8.3 Visualizing and Comparing Machine Learning Predictions to Improve Human-

AI Teaming on the Example of Cell Lineage

Participants:

Jiayi Hong (Inria, Paris-Saclay, correspondent), Ross Maciejewski (Ari-
zona State University), Alain Trubuil (InraE Paris-Saclay), Tobias Isen-
berg (Inria, Paris-Saclay).

We visualize the predictions of multiple machine learning models to help biologists as they inter-
actively make decisions about cell lineage—the development of a (plant) embryo from a single ovum
cell. Based on a confocal microscopy dataset, traditionally biologists manually constructed the cell
lineage, starting from this observation and reasoning backward in time to establish their inheritance. To
speed up this tedious process, we make use of machine learning (ML) models trained on a database of
manually established cell lineages to assist the biologist in cell assignment. Most biologists, however,
are not familiar with ML, nor is it clear to them which model best predicts the embryo’s development.
We thus have developed a visualization system that is designed to support biologists in exploring and

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Inria Annual Report 2023

comparing ML models, checking the model predictions, detecting possible ML model mistakes, and
deciding on the most likely embryo development. To evaluate our proposed system, we deployed our
interface with six biologists in an observational study. Our results show that the visual representations
of machine learning are easily understandable, and our tool, LineageD+, could potentially increase
biologists’ working efficiency and enhance the understanding of embryos.

9 Bilateral contracts and grants with industry

9.1 Bilateral contracts with industry

Participants:

Jean-Daniel Fekete, Katerina Batziakoudi.

CIFRE PhD fellowship of Katerina Batziakoudi with the Company Berger-Levrault (2023–2026).

Participants:

Petra Isenberg, Florent Cabric.

Contract with PayAnalytics for joint work on the Inria exploratory action called “Equity Analytics”

(2022-2024)

10 Partnerships and cooperations

10.1 International research visitors

10.1.1 Visits of international scientists

Other international visits to the team

Nivan Ferreira

Participants:

Jean-Daniel Fekete, Christian Poli, Nivan Ferreira.

Status Assistant Professor

Institution of origin: Universidade Federal de Pernambuco

Country: Brazil

Dates: 5/02/2024–31/01/2025

Context of the visit: Collaboration on Progressive Data Analysis and Visualization

Mobility program/type of mobility: DataIA invited professor program

Hendrik Strobelt

Participants:

Jean-Daniel Fekete, Petra Isenberg, Hendrik Strobelt.

Status Researcher

Institution of origin: IBM Research (Cambridge)

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Project AVIZ

Country: USA

Dates: 19/01/2023–21/01/2023

Context of the visit: Collaboration on Visual Analytics

Mobility program/type of mobility: self funded

10.1.2 Visits to international teams

Research stays abroad

Jean-Daniel Fekete

Visited institution: Monash University

Country: Australia

Dates: 16/10/2023–29/10/2023

Context of the visit: Scientific talk and possible collaborations

Mobility program/type of mobility: research stay

10.2 National initiatives

Program: ANR PRC (ANR-19-CE33-0012)

Project acronym: EMBER

Project title: Situated Visualizations for Personal Analytics

Duration: 2020 – 2023. Total funding: 712 k€

Coordinator: Pierre Dragicevic

Other partners: Inria Bordeaux, Sorbonne Université

Participants:

Participants:

Petra Isenberg, Lijie Yao.

Abstract: The Ember project studies how situated data visualization systems can help people use their
personal data (e.g., fitness and physiological data, energy consumption, banking transactions,
online social activity,. . . ) for their own benefit. Although personal data is generated in many areas
of daily life, it remains underused by individuals. Rarely is personal data subjected to an in-depth
analysis and used to inform daily decisions. This research aims to empower individuals to improve
their lives by helping them become advanced consumers of their own data. This research builds
on the area of personal visual analytics, which focuses on giving the general public effective and
accessible tools to get insights from their own data. Personal visual analytics is a nascent area of
research, but has so far focused on scenarios where the data visualization is far removed from the
source of the data it refers to. The goal of this project is to address the limitations of traditional
platforms of personal data analytics by exploring the potential of situated data visualizations. In a
situated data visualization, the data is directly visualized near the physical space, object, or person
it refers to. Situated data visualizations have many potential benefits: they can surface information
in the physical environment and allow viewers to interpret data in-context; they can be tailored to
highlight spatial connections between data and the physical environment, making it easier to make
decisions and act on the physical world in response to the insights gained; and they can embed
data into physical environments so that it remains visible over time, making it easier to monitor
changes, observe patterns over time and collaborate with other people. Website: ember.inria.fr/.

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Inria Annual Report 2023

Other projects:

• Health Data Hub “ParcoursVis” post-doctoral funding. Duration: 24 months. Total funding: 220k€.

Partners: Inria Saclay, Health Data Hub. See www.aviz.fr/Research/ParcoursVis.

• Participants:

Participants:

Jean-Daniel Fekete, Mickaël Sereno.

• AP-HP / Inria joint project URGE. Duration 48 months. Total funding: 520k€. Partners: Inria,
Hôpital Saint Antoine, Sorbonne Université. Coordinators: Xavier Leroy (Inria), Youri Yordanov
(AP-HP, Inserm, Sorbonne Université).

• Participants:

Participants:

Jean-Daniel Fekete, Mickaël Sereno.

• Action Exploratoire: EquityAnalytics: Visual Analytics for Pay Equity, Action Exploratoire. Post-
Doctoral Funding. Duration 24 months. Total funding: 106k€. Coordinator: Petra Isenberg.
Partners: PayAnalytics.

• Participants:

Participants:

Petra Isenberg, Florent Cabric.

11 Dissemination

11.1 Promoting scientific activities

• Jean-Daniel Fekete has introduced Graphs and their visualization to high-school students during

the Science Ouverte Seminar, Bobigny, Jun. 26, 2023

11.1.1 Scientific events: organisation

General chair, scientific chair

• Florent Cabric co-organized, with Vanessa Peña-Araya from Inria Ilda, the “Journée Visu” of the

French Visualization Community on June 22.

• Tobias Isenberg: Co-organizer of Shonan Seminar 173—TAT: Toughening the Foundation of Ab-

straction in Visualization Techniques

• Petra Isenberg,Florent Cabric, and MickaëlSereno organized “PreVis” on October 16th and 17th

2023.

Member of the organizing committees

• Tobias Isenberg: IEEE VIS 2023

• Petra Isenberg: EuroVis, Young Researcher Award Committee

• Petra Isenberg: IEEE VIS Steering Committee Co-Chair

• Petra Isenberg: IEEE VIS Test of Time Committee Member (for IEEE InfoVis papers)

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11.1.2 Scientific events: selection

Chair of conference program committees

• Tobias Isenberg: area chair for IEEE VIS 2023

Member of the conference program committees

• Jean-Daniel Fekete was PC member of the new “Undone Computer Science 2024” conference

• Jean-Daniel Fekete was PC member of the VIS 2023 conference

• Jean-Daniel Fekete was PC member of the EDBT/ICDT International Workshop on Big Data Visual

Exploration and Analytics 2023

• Tobias Isenberg: EuroVis 2023 full papers, EuroVis 2023 STARs, Eurographics 2023 STARs

Reviewer

• Tobias Isenberg: alt.VIS, CHI, EuroVis full papers, EuroVis STARs, Eurographics STARs, MolVA, ACM

SIGGRAPH, UIST

• Petra Isenberg: alt.VIS, CHI, CRC Press, ACM DIS, EuroVis full papers, EuroVis short papers, IEEE

PacificVis, IEEE VIS

• Lijie Yao: ACM CHI , alt.CHI, ETRA, EuroVis, HRI, IEEE VIS

• Florent Cabric: ACM CHI, IEEE VIS short and full papers

11.1.3 Journal

Member of the editorial boards

• Jean-Daniel Fekete: IEEE Transactions on Visualization and Computer Graphics,

• Tobias Isenberg: Computer Graphics Forum, Elsevier Computers & Graphics,

• Petra Isenberg: Computer Graphics Forum, Associate-Editor-in-Chief CG & A

Reviewer - reviewing activities

• Jean-Daniel Fekete: IEEE Transactions on Visualization and Computer Graphics, Computer Graph-

ics Forum

• Tobias Isenberg: Elsevier Computers & Graphics, ACM Transactions on Graphics, IEEE Transactions

on Visualization and Computer Graphics, Journal of Perceptual Imaging

• Petra Isenberg: Computer Science Review, IEEE Transactions on Visualization and Computer

Graphics

• Alaul Islam: IEEE Transactions on Visualization and Computer Graphics

11.1.4 Invited talks

• Jean-Daniel Fekete: Invited talk at the “Embodied Visualization” Lab at University of Monash,

Australia
Science Ouverte Seminar to high-school students (15 yo) on Graphs, Bobigny, France

• Tobias Isenberg: Peking University, China; Journées InraE-Inria, France; Keynote FAIRvis workshop,

Germany; Linköping University, Sweden; CSIG-VIS International Lecture Series, China

• Petra Isenberg: Seminar Series Talk at the University of Bergen

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Inria Annual Report 2023

11.1.5 Leadership within the scientific community

• Jean-Daniel Fekete: Co-Chair of the IEEE VIS Area Curation Committee

Member of the Scientific Committee of the French journal “Humanités Numériques”
Associate Editor in Chief of IEEE Transactions on Visualization and Computer Graphics (TVCG)
Member of the Publication Board of Eurographics

• Petra Isenberg: Steering Committee BELIV workshop series, Co-chair IEEE VIS Steering Committee

• Tobias Isenberg: Steering Committee BELIV workshop series

11.1.6 Scientific expertise

• Tobias Isenberg: ANR

• Petra Isenberg: NSERC, Mitacs, the Wallenberg Foundation

11.2 Teaching - Supervision - Juries

11.2.1 Teaching

• Master: Jean-Daniel Fekete, Visual Data Analysis at the Parisian Master of Research in Computer
Science with Julien Tierny. 25 hours. Joint Master with Univ. Paris-Cité, ENS Ulm, Univ. Paris-Saclay,
Institut Polytechnique de Paris.

• Crash course: Jean-Daniel Fekete, on HCI and Visualization, 6h, to IRT SystemX

• Master: Petra Isenberg, Lijie Yao, “Interactive Information Visualization”, 36.5h equivalent TD (total,

co-taught with Anastasia Bezerianos), M1/M2, Université Paris-Saclay.

• Master: Petra Isenberg, “Visual Analytics”, 60h equivalent TD (co-taught with Natkamon Tovanich),

M2, Centrale Supelec.

• Licence: Tobias Isenberg, “Introduction to Computer Graphics”, 18h en équivalent TD, L3, Polytech

Paris-Saclay, France.

• Master: Tobias Isenberg, “Photorealistic Rendering/Advanced Computer Graphics”, 28h en équival-

ent TD, M2, Polytech Paris-Saclay, France.

• Master: Tobias Isenberg, Jiayi Hong, “Data Visualization”, 36h en équivalent TD, M2, Centrale-

Supélec, France.

• Master: Frédéric Vernier, class and lab class. “Information visualization”, 24h en équivalent TD, M2

ISC, Faculté des sciences d’Orsay. Univ. Paris-Saclay, France.

• Master: Frédéric Vernier, class and lab class. “Web development with node.js”, 21h en équivalent

TD, M2 HCI, Faculté des sciences d’Orsay. Univ. Paris-Saclay, France.

• Licence: Mickaël Sereno, tutorial sessions for “Introduction to Computer Graphics”, 24h en équival-

ent TD, ET3 (eq. L3), Polytech Paris-Saclay, France.

• Licence: Mickaël Sereno, tutorial sessions for “Informatique Graphique 3D”, 40h en équivalent TD,

équivalent L3, Ecole Polytechnique Paris-Saclay, France.

• Licence: Frédéric Vernier, class and lab class. “Introduction to Computer Graphics”, 42h en équival-

ent TD, L1-MI, Faculté des sciences d’Orsay. Univ. Paris-Saclay, France.

• Licence: Frédéric Vernier, class and lab class. “Advanced Computer Graphics”, 36h en équivalent

TD, L2-MI, Faculté des sciences d’Orsay. Univ. Paris-Saclay, France.

• Licence: Frédéric Vernier, class and lab class. “Web development”, 42h en équivalent TD, L3

computer sciences, Faculté des sciences d’Orsay. Univ. Paris-Saclay, France.

Project AVIZ

11.2.2 Supervision

21

• PhD in progress: Katerinal Batziakoudi, Visualization of Budget and Accounting Data,Univ. Paris-

Saclay, defense planned for december 2026, Jean-Daniel Fekete.

• PhD in progress: Yucheng Lu, Hybrid Scientific Data Exploration, Univ. Paris-Saclay, defense

planned for December 2026, Tobias Isenberg.

• PhD in progress: Deng Luo, Abstraction of Visual Representations for DNA Nanotechnology and

Protein Data, KAUST, Saudi-Arabia, Tobias Isenberg (co-supervisor).

• PhD in progress: Tobias Rau, Interactive AR/VR Visualization of Molecular Simulation Data, Univ.

Stuttgart, Germany, Tobias Isenberg (co-supervisor).

• PhD in progress: Tingying He, A Design Space for Visual Data Mapping for Low-Color Displays, Univ.

Paris-Saclay, defense planned for December 2024, Tobias Isenberg.

• PhD in progress: Gabriela Carolina Molina Leon, Multimodal and Collaborative Interaction for

Visual Data Exploration, defense planned for March 2024, Petra Isenberg (co-advisor).

• PhD completed: Lijie Yao, Situated Visualization in Motion, Univ. Paris-Saclay, defended on Decem-

ber 18, 2023, Petra Isenberg.

• PhD completed: Alaul Islam, Visualizations for Smartwatches and Fitness Trackers, Univ. Paris-

Saclay, defended on March 16, 2023, Petra Isenberg.

• PhD completed: Dr. Jiayi Hong, Machine Learning Supported Interactive Visualization of Hybrid
3D and 2D Data for the Example of Plant Cell Lineage Specification, Univ. Paris-Saclay, defended
successfully on Feb. 14, 2023, Tobias Isenberg.

• Masters in progress: Anne-Flore Cabouat, A Validated Scale for Measuring the Readability of

Visualizations, Univ. Paris Cité, Tobias Isenberg (supervisor), Petra Isenberg (co-supervisor).

• Masters completed: Yucheng Lu, Historic Sites: Combining Augmented Reality with a Mobile Device

for a Hybrid Exploration Experience, Univ. Paris-Saclay, Tobias Isenberg

12 Scientific production

12.1 Major publications

[1] L. Battle, P. Eichmann, M. Angelini, T. Catarci, G. Santucci, Y. Zheng, C. Binnig, J.-D. Fekete and
D. Moritz. ‘Database Benchmarking for Supporting Real-Time Interactive Querying of Large Data’.
In: SIGMOD ’20 - International Conference on Management of Data. Proceedings of the 2020 ACM
SIGMOD International Conference on Management of Data. Portland, OR, United States: ACM,
June 2020, pp. 1571–1587. DOI: 10.1145/3318464.3389732. URL: https://hal.inria.fr/hal-
02556400.

[2] F. Cabric, M. Vilborg Bjarnadóttir, A.-F. Cabouat and P. Isenberg. ‘Open Questions about the Visu-
alization of Sociodemographic Data’. In: IEEE VIS: Visualization & Visual Analytics Conference
- Workshop on Visualization for Social Good (VIS4Good). VIS4Good 2023 - IEEE Workshop on
Visualization for Social Good. Melbourne, Australia, 22nd Oct. 2023. DOI: 10.1109/VIS4Good6021
8.2023.00010. URL: https://inria.hal.science/hal-04184101.

[3] T. He, P. Isenberg, R. Dachselt and T. Isenberg. ‘BeauVis: A Validated Scale for Measuring the
Aesthetic Pleasure of Visual Representations’. In: IEEE Transactions on Visualization and Computer
Graphics 29.1 (Jan. 2023), pp. 363–373. DOI: 10.1109/TVCG.2022.3209390. URL: https://hal.s
cience/hal-03763559.

[4] B. Lee, R. Dachselt, P. Isenberg and E. K. Choe. Mobile Data Visualization. 0. Chapman and Hall/CRC,
Dec. 2021, p. 346. DOI: 10.1201/9781003090823. URL: https://hal.inria.fr/hal-03524082.

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[5] A. Patil, G. Richer, C. Jermaine, D. Moritz and J.-D. Fekete. ‘Studying Early Decision Making with
Progressive Bar Charts’. In: IEEE Transactions on Visualization and Computer Graphics 29.1 (Jan.
2023), pp. 407–417. DOI: 10.1109/TVCG.2022.3209426. URL: https://inria.hal.science/h
al-03738461.

[6] G. Richer, A. Pister, M. Abdelaal, J.-D. Fekete, M. Sedlmair and D. Weiskopf. ‘Scalability in Visu-
alization’. In: IEEE Transactions on Visualization and Computer Graphics (29th Dec. 2022). DOI:
10.1109/TVCG.2022.3231230. URL: https://hal.inria.fr/hal-03820394.

[7] W. Willett, B. A. Aseniero, S. Carpendale, P. Dragicevic, Y. Jansen, L. Oehlberg and P. Isenberg.
‘Perception! Immersion! Empowerment! Superpowers as Inspiration for Visualization’. In: IEEE
Transactions on Visualization and Computer Graphics 28.1 (Jan. 2022), pp. 22–32. DOI: 10.1109
/TVCG.2021.3114844. URL: https://hal.inria.fr/hal-03342677.

[8] L. Yao, A. Bezerianos, R. Vuillemot and P. Isenberg. ‘Visualization in Motion: A Research Agenda
and Two Evaluations’. In: IEEE Transactions on Visualization and Computer Graphics 28.10 (1st Oct.
2022), pp. 3546–3562. DOI: 10.1109/TVCG.2022.3184993. URL: https://hal.inria.fr/hal-
03698837.

12.2 Publications of the year

International journals

[9] M. Alkadi, V. Serrano, J. Scott-Brown, C. Plaisant, J.-D. Fekete, U. Hinrichs and B. Bach. ‘Under-
standing Barriers to Network Exploration with Visualization: A Report from the Trenches’. In: IEEE
Transactions on Visualization and Computer Graphics 29.1 (2023), pp. 907–917. DOI: 10.1109
/TVCG.2022.3209487. URL: https://inria.hal.science/hal-03738500.

[10] M. Brossier, R. Skånberg, L. Besançon, M. Linares, T. Isenberg, A. Ynnerman and A. Bock. ‘Moliverse:
Contextually embedding the microcosm into the universe’. In: Computers and Graphics 112 (May
2023), pp. 22–30. DOI: 10.1016/j.cag.2023.02.006. URL: https://inria.hal.science/hal-
04028899.

[11] F. Cabric, M. Vilborg Bjarnadóttir, M. Ling, G. Linda Rafnsdóttir and P. Isenberg. ‘Eleven Years
of Gender Data Visualization: A Step Towards More Inclusive Gender Representation’. In: IEEE
Transactions on Visualization and Computer Graphics 30.1 (1st Jan. 2024), pp. 316–326. DOI: 10.11
09/TVCG.2023.3327369. URL: https://inria.hal.science/hal-04179018.

[12] T. He, P. Isenberg, R. Dachselt and T. Isenberg. ‘BeauVis: A Validated Scale for Measuring the
Aesthetic Pleasure of Visual Representations’. In: IEEE Transactions on Visualization and Computer
Graphics 29.1 (Jan. 2023), pp. 363–373. DOI: 10.1109/TVCG.2022.3209390. URL: https://hal.s
cience/hal-03763559.

[13] T. He, Y. Zhong, P. Isenberg and T. Isenberg. ‘Design Characterization for Black-and-White Textures
in Visualization’. In: IEEE Transactions on Visualization and Computer Graphics 30.1 (1st Jan. 2024),
pp. 1019–1029. DOI: 10.1109/TVCG.2023.3326941. URL: https://hal.science/hal-0416790
0.

[14]

[15]

[16]

J. Hong, R. Hnatyshyn, E. A. D. Santos, R. Maciejewski and T. Isenberg. ‘A Survey of Designs for
Combined 2D+3D Visual Representations’. In: IEEE Transactions on Visualization and Computer
Graphics (2024). URL: https://inria.hal.science/hal-04382271.

J. Hong, R. Maciejewski, A. Trubuil and T. Isenberg. ‘Visualizing and Comparing Machine Learning
Predictions to Improve Human-AI Teaming on the Example of Cell Lineage’. In: IEEE Transactions
on Visualization and Computer Graphics (2024). DOI: 10.1109/TVCG.2023.3302308. URL: https:
//inria.hal.science/hal-04212205.

J. Hong, X. Wang, A. Everitt and A. Roudaut. ‘Interacting with actuated walls: Exploring applications
and input types’. In: International Journal of Human-Computer Studies 172 (Apr. 2023), p. 102986.
DOI: 10.1016/j.ijhcs.2022.102986. URL: https://inria.hal.science/hal-03932171.

Project AVIZ

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[17] D. Kouˇril, O. Strnad, P. Mindek, S. Halladjian, T. Isenberg, M. E. Gröller and I. Viola. ‘Molecument-
ary: Adaptable Narrated Documentaries Using Molecular Visualization’. In: IEEE Transactions on
Visualization and Computer Graphics 29.3 (Mar. 2023), pp. 1733–1747. DOI: 10.1109/TVCG.2021
.3130670. URL: https://inria.hal.science/hal-03451509.

[18] D. Kut’ák, P.-p. Vázquez, T. Isenberg, M. Krone, M. Baaden, J. Byška, B. Kozlíková and H. Miao. ‘State
of the Art of Molecular Visualization in Immersive Virtual Environments’. In: Computer Graphics
Forum 42.6 (Sept. 2023), e14738:1–e14738:29. DOI: 10.1111/cgf.14738. URL: https://inria.h
al.science/hal-04006531.

[19] G. M. León, P. Isenberg and A. Breiter. ‘Eliciting Multimodal and Collaborative Interactions for
Data Exploration on Large Vertical Displays’. In: IEEE Transactions on Visualization and Computer
Graphics 30.2 (1st Feb. 2024), pp. 1624–1637. DOI: 10.1109/TVCG.2023.3323150. URL: https:
//inria.hal.science/hal-04365019.

[20]

J. W. Nam, T. Isenberg and D. Keefe. ‘V-Mail: 3D-Enabled Correspondence about Spatial Data on
(Almost) All Your Devices’. In: IEEE Transactions on Visualization and Computer Graphics (2024).
DOI: 10.1109/TVCG.2022.3229017. URL: https://inria.hal.science/hal-03924707.

[21] A. Patil, G. Richer, C. Jermaine, D. Moritz and J.-D. Fekete. ‘Studying Early Decision Making with
Progressive Bar Charts’. In: IEEE Transactions on Visualization and Computer Graphics 29.1 (Jan.
2023), pp. 407–417. DOI: 10.1109/TVCG.2022.3209426. URL: https://inria.hal.science/h
al-03738461.

[22] A. Pister, C. Prieur and J.-D. Fekete. ‘ComBiNet: Visual Query and Comparison of Bipartite Mul-
tivariate Dynamic Social Networks’. In: Computer Graphics Forum 42.1 (5th Jan. 2023), pp. 290–304.
DOI: 10.1111/cgf.14731. URL: https://inria.hal.science/hal-03927450.

[23] A. Ulmer, M. Angelini, J.-D. Fekete, J. Kohlhammer and T. May. ‘A Survey on Progressive Visu-
alization’. In: IEEE Transactions on Visualization and Computer Graphics (1st Jan. 2024). DOI:
10.1109/TVCG.2023.3346641. URL: https://inria.hal.science/hal-04361344.

[24] L. Yao, R. Vuillemot, A. Bezerianos and P. Isenberg. ‘Designing for Visualization in Motion: Embed-
ding Visualizations in Swimming Videos’. In: IEEE Transactions on Visualization and Computer
Graphics (2024). DOI: 10.1109/tvcg.2023.3341990. URL: https://hal.science/hal-04364
838.

[25] L. Zhao, T. Isenberg, F. Xie, H.-N. Liang and L. Yu. ‘MeTACAST: Target-and Context-aware Spatial
Selection in VR’. In: IEEE Transactions on Visualization and Computer Graphics 30.1 (1st Jan. 2024),
pp. 480–494. DOI: 10.1109/TVCG.2023.3326517. URL: https://inria.hal.science/hal-04
196163.

International peer-reviewed conferences

[26] T. Blascheck, L. Besançon, A. Bezerianos, B. Lee, A. Islam, T. He and P. Isenberg. ‘Studies of Part-
to-Whole Glanceable Visualizations on Smartwatch Faces’. In: The IEEE Pacific Visualization
Symposium (PacificVis). Los Alamitos, United States, 2023. DOI: 10.1109/PacificVis56936.202
3.00028. URL: https://inria.hal.science/hal-04018448.

[27] F. Cabric, M. Vilborg Bjarnadóttir, A.-F. Cabouat and P. Isenberg. ‘Open Questions about the Visu-
alization of Sociodemographic Data’. In: IEEE VIS: Visualization & Visual Analytics Conference
- Workshop on Visualization for Social Good (VIS4Good). VIS4Good 2023 - IEEE Workshop on
Visualization for Social Good. Melbourne, Australia, 22nd Oct. 2023. DOI: 10.1109/VIS4Good6021
8.2023.00010. URL: https://inria.hal.science/hal-04184101.

[28] T. He, P. Isenberg and T. Isenberg. ‘Data Embroidery with Black-and-White Textures’. In: alt.VIS
Workshop at IEEE VIS 2023. Melbourne, Australia, 22nd Oct. 2023. URL: https://hal.science/h
al-04197527.

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Scientific book chapters

[29] M. Le Goc, C. Perin, S. Follmer, J.-D. Fekete and P. Dragicevic. ‘Zooids’. In: Making with Data:
Physical Design and Craft in a Data-Driven World. A K Peters/CRC Press, 2023. URL: https://inr
ia.hal.science/hal-03978632.

Doctoral dissertations and habilitation theses

[30]

J. Hong. ‘Machine Learning Supported Interactive Visualization of Hybrid 3D and 2D Data for
the Example of Plant Cell Lineage Specification’. Université Paris-Saclay, 14th Feb. 2023. URL:
https://theses.hal.science/tel-04016701.

[31] M. A. Islam. ‘Visualizations for Smartwatches and Fitness Trackers’. Université Paris-Saclay, 16th Mar.

2023. URL: https://theses.hal.science/tel-04047471.

Reports & preprints

[32] G. Arroyo, D. Martín and T. Isenberg. Interpolation of Point Distributions for Digital Stippling. 2023.
DOI: 10.48550/arXiv.2307.00938. URL: https://inria.hal.science/hal-04212086.

[33] A.-F. Cabouat, T. He, P. Isenberg and T. Isenberg. Pondering the reading of visual representations.

23rd June 2023. URL: https://inria.hal.science/hal-04240900.

[34] A. Islam, T. He, A. Bezerianos, T. Blascheck, B. Lee and P. Isenberg. Visualizing Information on
Smartwatch Faces: A Review and Design Space. 2023. DOI: 10.48550/arXiv.2310.16185. URL:
https://inria.hal.science/hal-04365013.

[35] G. M. León, P. Isenberg and A. Breiter. Talking to Data Visualizations: Opportunities and Challenges.
2023. DOI: 10.48550/arXiv.2309.09781. URL: https://inria.hal.science/hal-04365017.

Other scientific publications

[36]

J. Chen, M. Chen, S. Chen, S.-H. Hong, W. Huang, T. Isenberg, K. Klein, T. Möller, G. Reina, P.
Ruchikachorn, M. Schwärzler, I. Viola, Y. Wang, L. Yu and X. Yuan. TAT: Toughening the Foundation
of Abstraction in Visualization Techniques: NII Shonan Meeting Report. Shonan, Japan, 11th Sept.
2023. URL: https://inria.hal.science/hal-04325828.

12.3 Cited publications

[37] S. Agarwal, H. Milner, A. Kleiner, A. Talwalkar, M. Jordan, S. Madden, B. Mozafari and I. Stoica.
‘Knowing when You’Re Wrong: Building Fast and Reliable Approximate Query Processing Systems’.
In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data. SIG-
MOD ’14. Snowbird, Utah, USA: ACM, 2014, pp. 481–492. DOI: 10.1145/2588555.2593667. URL:
http://doi.acm.org/10.1145/2588555.2593667.

[38] S. K. Badam, N. Elmqvist and J.-D. Fekete. ‘Steering the Craft: UI Elements and Visualizations for
Supporting Progressive Visual Analytics’. In: Computer Graphics Forum. Eurographics Conference
on Visualization (EuroVis 2017) 36.3 (June 2017), pp. 491–502. DOI: 10.1111/cgf.13205. URL:
https://hal.inria.fr/hal-01512256.

[39] A. Bezerianos, P. Dragicevic, J.-D. Fekete, J. Bae and B. Watson. ‘GeneaQuilts: A System for Exploring
Large Genealogies’. In: IEEE Transactions on Visualization and Computer Graphics 16.6 (Oct. 2010),
pp. 1073–1081. DOI: 10.1109/TVCG.2010.159. URL: https://hal.inria.fr/inria-00532939
.

[40] F. Bucchieri, L. Yao and P. Isenberg. Situated Visualization in Motion for Video Games. Posters of the
European Conference on Visualization (EuroVis). Poster. June 2022. DOI: 10.2312/evp.20221119.
URL: https://inria.hal.science/hal-03694019.

[41] F. Bucchieri, L. Yao and P. Isenberg. ‘Visualization in Motion in Video Games for Different Types of
Data’. In: Journée Visu 2022. Bordeaux, France, June 2022. URL: https://inria.hal.science/h
al-03700418.

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25

[42]

[43]

S. K. Card, J. D. Mackinlay and B. Shneiderman, eds. Readings in information visualization: using
vision to think. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1999.

J. Chen, P. Isenberg, R. S. Laramee, T. Isenberg, M. Sedlmair, T. M¯oller and H.-W. Shen. ‘Not As
Easy As You Think-Experiences and Lessons Learnt from Creating a Visualization Image Typology’.
working paper or preprint. Oct. 2022. DOI: 10.48550/arXiv.2209.07533. URL: https://inria
.hal.science/hal-03812031.

[44] X. Chen, J. Zhang, C.-W. Fu, J.-D. Fekete and Y. Wang. ‘Pyramid-based Scatterplots Sampling
for Progressive and Streaming Data Visualization’. In: IEEE Transactions on Visualization and
Computer Graphics 28.1 (Jan. 2022), pp. 593–603. DOI: 10 . 1109 / TVCG . 2021 . 3114880. URL:
https://inria.hal.science/hal-03360776.

[45] S. Di Bartolomeo, A. Pister, P. Buono, C. Plaisant, C. Dunne and J.-D. Fekete. ‘Six Methods for
Transforming Layered Hypergraphs to Apply Layered Graph Layout Algorithms’. In: Computer
Graphics Forum. EuroVis 2022 - Conference Proceedings 41.3 (June 2022), pp. 1467–8659. DOI:
10.1111/cgf.14538. URL: https://inria.hal.science/hal-03694781.

[46]

[47]

J.-D. Fekete, D. Fisher, A. Nandi and M. Sedlmair. ‘Progressive Data Analysis and Visualization
(Dagstuhl Seminar 18411)’. In: Dagstuhl Reports 8.10 (2018), pp. 1–32. DOI: 10.4231/DagRep.8.1
.1. URL: https://doi.org/10.4231/DagRep.8.1.1.

J.-D. Fekete and R. Primet. ‘Progressive Analytics: A Computation Paradigm for Exploratory Data
Analysis’. https://arxiv.org/abs/1607.05162 - working paper or preprint. July 2016. URL:
https://hal.inria.fr/hal-01361430.

[48] M. Garofalakis, J. Gehrke and R. Rastogi. Data stream management: processing high-speed data

streams. Springer, 2016.

[49] B. K. Ghosh and P. K. Sen. Handbook of sequential analysis. CRC Press, 1991.

[50] A. Islam, R. Aravind, T. Blascheck, A. Bezerianos and P. Isenberg. ‘Preferences and Effectiveness
of Sleep Data Visualizations for Smartwatches and Fitness Bands’. In: CHI 2022 - Conference on
Human Factors in Computing Systems. New Orleans, LA, United States, Apr. 2022. DOI: 10.1145/3
491102.3501921. URL: https://inria.hal.science/hal-03587029.

[51] A. Islam, T. Blascheck and P. Isenberg. Context Specific Visualizations on Smartwatches. Posters of
the European Conference on Visualization (EuroVis). Poster. June 2022. DOI: 10.2312/evp.20221
122. URL: https://inria.hal.science/hal-03694026.

[52] A. Islam, L. Yao, A. Bezerianos, T. Blascheck, T. He, B. Lee, R. Vuillemot and P. Isenberg. ‘Reflections
on Visualization in Motion for Fitness Trackers’. In: MobileHCI 2022 Workshop on New Trends in HCI
and Sports. Vancouver, Canada, Sept. 2022. URL: https://inria.hal.science/hal-03775633.

[53] Y. Jansen. ‘Physical and Tangible Information Visualization’. PhD thesis. Université Paris Sud-Paris

XI, 2014.

[54]

[55]

J. Jo, J. Seo and J.-D. Fekete. ‘PANENE: A Progressive Algorithm for Indexing and Querying Approx-
imate k-Nearest Neighbors’. In: IEEE Transactions on Visualization and Computer Graphics 26.2
(Feb. 2020), pp. 1347–1360. DOI: 10.1109/TVCG.2018.2869149. URL: https://hal.inria.fr
/hal-01855672.

J. Jo, F. Vernier, P. Dragicevic and J.-D. Fekete. ‘A Declarative Rendering Model for Multiclass Density
Maps’. In: IEEE Transactions on Visualization and Computer Graphics 25.1 (Jan. 2019), pp. 470–480.
DOI: 10.1109/TVCG.2018.2865141. URL: https://hal.inria.fr/hal-01848427.

[56] E. Jones, T. Oliphant, P. Peterson et al. SciPy: Open source scientific tools for Python. [Online; accessed

<today>]. 2001. URL: http://www.scipy.org/.

[57] D. Kahneman. Thinking, Fast and Slow. Farrar, Straus and Giroux, 2011.

[58]

S. Latif and F. Beck. ‘VIS Author Profiles: Interactive Descriptions of Publication Records Combining
Text and Visualization’. In: IEEE Transactions on Visualization and Computer Graphics 25.1 (Jan.
2019), pp. 152–161. DOI: 10.1109/TVCG.2018.2865022.

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[59] H. Miao, E. De Llano, T. Isenberg, M. E. Gröller, I. Barišic and I. Viola. ‘DimSUM: Dimension and
Scale Unifying Maps for Visual Abstraction of DNA Origami Structures’. In: Computer Graphics
Forum 37.3 (June 2018), pp. 403–413. DOI: 10.1111/cgf.13429. URL: https://hal.inria.fr/h
al-01795734.

[60] H. Miao, T. Klein, D. Kouˇril, P. Mindek, K. Schatz, M. E. Gröller, B. Kozlíková, T. Isenberg and I. Viola.
‘Multiscale Molecular Visualization’. In: Journal of Molecular Biology 431.6 (Mar. 2019), pp. 1049–
1070. DOI: 10.1016/j.jmb.2018.09.004. URL: https://inria.hal.science/hal-01873724.

[61] M. Miao, E. De Llano, J. Sorger, Y. Ahmadi, T. Kekic, T. Isenberg, M. E. Gröller, I. Barišic and
I. Viola. ‘Multiscale Visualization and Scale-Adaptive Modification of DNA Nanostructures’. In:
IEEE Transactions on Visualization and Computer Graphics 24.1 (Jan. 2018), pp. 1014–1024. DOI:
10.1109/TVCG.2017.2743981. URL: https://hal.inria.fr/hal-01581203.

[62] R. B. Miller. ‘Response Time in Man-computer Conversational Transactions’. In: Proc. of the Fall
Joint Computer Conference, Part I. San Francisco, California: ACM, 1968, pp. 267–277. DOI: 10.114
5/1476589.1476628. URL: http://doi.acm.org/10.1145/1476589.1476628.

[63] A. Pister, P. Buono, J.-D. Fekete, C. Plaisant and P. Valdivia. ‘Integrating Prior Knowledge in Mixed
Initiative Social Network Clustering’. In: IEEE Transactions on Visualization and Computer Graphics
27.2 (Feb. 2021), pp. 1775–1785. DOI: 10.1109/TVCG.2020.3030347. URL: https://hal.inria
.fr/hal-02566438.

[64] R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical

Computing. Vienna, Austria, 2019. URL: https://www.R-project.org.

[65] G. Richer, A. Pister, M. Abdelaal, J.-D. Fekete, M. Sedlmair and D. Weiskopf. ‘Scalability in Visualiz-
ation’. In: IEEE Transactions on Visualization and Computer Graphics (Dec. 2022). DOI: 10.1109
/TVCG.2022.3231230. URL: https://inria.hal.science/hal-03820394.

[66] V. Serrano Molinero, B. Bach, C. Plaisant, N. Dufournaud and J.-D. Fekete. ‘Understanding the Use
of The Vistorian: Complementing Logs with Context Mini-Questionnaires’. In: Visualization for the
Digital Humanities. Phoenix, United States, Oct. 2017. URL: https://hal.inria.fr/hal-01650
259.

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<scratchpad>
- Main topic: Analysis and Visualization (AVIZ) project at Inria Saclay Centre
- Key points:
  - AVIZ is a multidisciplinary project focusing on improving data exploration methods, techniques, and tools based on interactive visualization.
  - The project aims to address scalability issues in data analysis and visualization, particularly in the context of large datasets and complex analytical methods.
  - AVIZ is organized around four research themes: Progressive Data Analysis and Scalability, Physicality in Input and Output, Perception, Cognition, and Decision-Making, and Methodologies for Visualization Research.
  - The project has collaborated with various institutions and partners, including CNRS, Université Paris-Saclay, and industry partners.
- Structure: The document is organized into sections, including an activity report, project-team information, research themes, and application domains.
- Potential artifacts to ignore: Web-related elements, such as links, HTML tags, and other web-related elements.
</scratchpad>

<final_summary>
The AVIZ project at Inria Saclay Centre is a multidisciplinary research initiative focused on improving data exploration methods, techniques, and tools based on interactive visualization. The project aims to address scalability issues in data analysis and visualization, particularly in the context of large datasets and complex analytical methods. AVIZ is organized around four research themes, including Progressive Data Analysis and Scalability, Physicality in Input and Output, Perception, Cognition, and Decision-Making, and Methodologies for Visualization Research. The project has collaborated with various institutions and partners, including CNRS, Université Paris-Saclay, and industry partners, to advance the field of visualization and data analysis.
The AVIZ project at Inria Saclay Centre is a multidisciplinary research initiative focused on improving data exploration methods, techniques, and tools based on interactive visualization. The project aims to address scalability issues in data analysis and visualization, particularly in the context of large datasets and complex analytical methods. AVIZ is organized around four research themes, including Progressive Data Analysis and Scalability, Physicality in Input and Output, Perception, Cognition, and Decision-Making, and Methodologies for Visualization Research. The project has collaborated with various institutions and partners, including CNRS, Université Paris-Saclay, and industry partners, to advance the field of visualization and data analysis.