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"ACM SIGCHI General Interest Announcements (Mailing List)" <[log in to unmask]>
Fri, 27 Jun 2014 16:21:56 -0400
"G.Elisabeta Marai" <[log in to unmask]>
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"G.Elisabeta Marai" <[log in to unmask]>
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* Death of the Desktop – Envisioning Visualization without Desktop Computing
* Provenance for Sensemaking
* DECISIVe : Dealing with Cognitive Biases in Visualisations
* Towards An Open Visualization Literacy Testing Platform
* Visualization for Predictive Analytics
* Visualizing Electronic Health Record Data

For submission deadlines, please see links below.

*Death of the Desktop – Envisioning Visualization without Desktop Computing*

Yvonne Jansen, Petra Isenberg, Jason Dykes, Sheelagh Carpendale, Sriram
Subramanian, Daniel Keefe

The Desktop computer is dead. Monitors sit on desks, unplugged - hosting
layers of Post-It notes or gathering dust as a retro emergency low-light
mirror. Visualization is colourful, big, tangible, nosy, interactive,
compelling and everywhere. It supports all sorts of creative activity and
is key to problem solving in education, science, government and industry.

But how?

What is your 'imagined future' for visualization?

We will be exploring possible visualization scenarios with short but rich
scenarios in which designers, practitioners and researchers creatively
explore opportunities for 'beyond-the-desktop' visualization. We will be
discussing these and using them to develop the community’s perspective on
the future of VIS.

For the detailed workshop description, submission details, and program
please visit the workshop website at

*Provenance for Sensemaking*

Kai Xu, Simon Attfield, T.J. Jankun-Kelly

During complex sensemaking and analysis task, it can be valuable to
maintain a history of the processes and transformations involved - referred
to as ‘provenance’ information. Provenance information can be a resource
for "reflection-in-action" during analyses, for supporting planning and
reframing of objectives and scope. It can also be a resource after the
event, supporting the interpretation of claims, audit, accountability or

There has been considerable work on capturing and visualizing of ‘data
provenance’, which focuses on data collection and computation, and
‘analytic provenance’, which captures the interactive data exploration
process. However, there is limited work of utilizing these provenance
information to support sensemaking, in terms of improving its efficacy and
avoid pitfalls such as data quality issue and human bias.

This workshop aims to bring together researchers involved in visual
analytics and various aspects of sensemaking to consider emerging
positions, questions, and findings related to the capture, processing,
representation and use of provenance information to support complex
sensemaking tasks. The emphasis is on discussion and collaboration, with a
goal to produce a paper describing the state-of-the-art of provenance for
sensemaking after the workshop.

*DECISIVe: Dealing with Cognitive Biases in Visualisations*

Geoffrey Ellis, David Peebles, Donald Kretz, Gaëlle Lortal

Our inherent reliance on mental shortcuts, or heuristics, sometimes results
in deviations in judgment from what rational decision models would predict.
These deviations are known as cognitive biases. Heuristics allow us to make
“good enough” decisions without expending all of our cognitive effort on
the task, however, in critical decision environments, “good enough” is
often NOT good enough. Visualization tools are increasingly adept at making
sense of complex data, but researchers who study cognitive biases have come
to realize that the quality of decisions made with these tools are often
impaired because tool designers fail to address how heuristics and biases
operate in a human-computer interactive setting. The aim of this workshop
is to bring together a wide range of researchers and developers from
domains such as information visualization, visual analytics and cognitive
psychology to explore some of the ways in which biases impact user
performance and share ideas and experiences about practical ways to reduce
or overcome these potentially harmful effects in the systems we build.

*Towards An Open Visualization Literacy Testing Platform*

Sung-Hee Kim, Jeremy Boy, Sukwon Lee, Ji Soo Yi, and Niklas Elmqvist

We propose a hands-on workshop where participants will learn about and
discuss visualization literacy by actually designing and evaluating
questions for a visualization literacy measure. Though the value of
information visualization is becoming apparent to a broad audience,
visualization researchers often acknowledge that people have different
levels of understanding of visualization techniques. In other words, our
understanding of how users interpret visualizations has not caught up with
design and technical developments, and even the concept of visualization
literacy is still debated. Different domains of research, such as
mathematics education, cognitive science, and psychology, have been
approaching this problem within their domain. We believe that researchers
in information visualization and visual analytics should lead the effort in
defining the concept, and in creating valid and practical measurement tools.
The goal of our workshop is to take a step in this direction by developing
a better understanding of visualization literacy, identifying possible
metrics for evaluation, and raising new questions for future research
through the design and evaluation of visualization literacy tests. The
outcome of our workshop will be a participatory web-platform for
collectively created visualization literacy tests and questionnaires that
can directly be used by researchers in our community.

*Visualization for Predictive Analytics*

E. Bertini, A. Perer, R. Maciejewski, J. Sun

One of the surprising facts of much current visualization research is that
prediction does not often play a significant role. Most visualization
research seems to focus exclusively on data analysis and presentation, with
little support for predictive analytics and the numerous models researchers
have developed for this purpose. Upon reflection, this comes as a surprise
as many scientific endeavors and many business problems are mostly
concerned with prediction.  Looking more closely at the recent advancements
(and tremendous popularity) of Data Science, one may recognize that the
vast majority of problems addressed involve some form of prediction and
modelling. Notable examples are: prediction of drug effectiveness in drug
development, prediction of diseases in healthcare, prediction of crime in
city management.
Our goals are to increase the awareness about this interesting opportunity
for visualization research, collect and compare examples of existing and
ongoing research in this area and to for visual analytics researchers.  The
workshop will allow participants to showcase their existing research and
ideas and to learn and reflect on the latest advances in visualization of
predictive models.

*Visualizing Electronic Health Record Data*

Catherine Plaisant, Silvia Miksch, Theresia Gschwandtner, Sana Malik

Electronic Health Record (EHR) databases contain millions of patient
records including events such as diagnoses, test results, or medication
prescriptions. These records are an invaluable data source for clinical
research and improvement of clinical quality, as they provide longitudinal
health information about patient populations. The use of EHR databases
could be dramatically improved if easy-to-use interfaces allowed clinical
researchers and quality improvement analysts to explore complex patterns in
order to build and test hypotheses regarding the benefits, risks, and
appropriateness of treatments or medication regimens.

Novel strategies in information visualization and visual analytics are
needed.  The interest in this topic is growing at very rapid pace and is
very interdisciplinary by nature, both in term of field (medicine and
computer science) but also research environment (academic research as well
as industry and government agencies). Because of the European location of
the conference, we have a unique opportunity to create bridges and explore
new collaborations between groups that would have never met otherwise.

Nathalie Henry Riche, Microsoft Research Redmond
Tobias Isenberg, INRIA Saclay – Île-de-France
Tobias Schreck, University of Konstanz
Zoė J. Wood, California Polytechnic

For further information, please email [log in to unmask]

-- Liz
G. Elisabeta Marai
Robotics Institute
School of Computer Science
Carnegie Mellon University

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