[Infovis] IEEE VIS 2014 CFP: Workshop on Visualization for Predictive Analytics

G.Elisabeta Marai g.elisabeta.marai at gmail.com
Mon Jul 7 23:09:16 CEST 2014




VIS'14 Capstone Speaker: Barbara Tversky (Stanford/Columbia)


Workshop  on Visualization for Predictive Analytics: Call For Participation

Co-Located with IEEE VIS'14 | Paris 9-14 Nov, 2014


SUBMISSION: Sep. 15, 2014

PARTICIPATION: The workshop is open to all VIS 2014 attendees

FOR INQUIRIES: predva.workshop at gmail.com

What is the use of visualization in prediction? How do people use
visualization in predictive modeling? How can we improve the state of the
art in predictive visual analytics?

We invite the submissions of *short technical or position papers* (between
1-4 pages) to explore the use of visualization in prediction and showcase
existing research.

TECHNICAL PAPERS - May include, but are not limited to the following:

Novel Visual Analytics or Visualization Techniques

* How to assist predictive development, evaluation and communication

* Visual analytics techniques for predictive models such as regression and

* Visual analytics techniques for clustering

* Visual analytics techniques for high-dimensional data

* Interactive visualization for refining predictive models

Applications and Case Studies

* Real-world problems and experiences from public sectors and industry

* Predictive visual analytics in business, technology, healthcare, finance,
telecommunications, etc.

* Predictive visual analytics applications in public sectors such as
government, development, security, etc.


* Real and synthetic data sets and benchmarks

* Taxonomies of predictive tasks in visualization

* Evaluation and testing in predictive visual analytics

POSITION PAPERS - May include, but are not limited to, visionary ideas
addressing topics such as:

* What's the use of visualization in prediction tasks?

* How can visualization help data scientists make sense of predictive

* How can end users be sure predictive models are made of domain-relevant

* How can visual analytics help researchers include their domain knowledge
into the modeling process?

* How do we build benchmark datasets and ground truth to objectively
compare different predictive model visualizations?

* What are good and suitable processes to ensure usefulness of predictive

* How can we use visual analytics to “explain” patterns derived from
predictive models?

* How can we use predictive models as discovery tools?

* How can visualization help modelers gain trust into their results?

* Is visualization needed at all?

* Where is the boundary between predictive, explanatory and exploratory

See http://predictive-workshop.github.io/ for more details.


Adam Perer, Enrico Bertini, Ross Maciejewski, Jimeng Sun

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

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