The IEEE Conference on Visual Analytics Science and Technology (IEEE
VAST 2017) is the leading international conference dedicated to
advances in visual analytics. The scope of the conference, co-located
at VIS with the annual IEEE Scientific Visualization Conference and
the IEEE Information Visualization Conference, includes both
fundamental research contributions within visual analytics as well as
applications of visual analytics in science, engineering, medicine,
health, media, business, social interaction, security and
investigative analysis, and other disciplines. We invite you to
participate in IEEE VAST 2017 by submitting your original research and
Following the successful two-track arrangement in previous years, IEEE
VAST 2017 will again feature an expanded set of accepted papers in two
TVCG Track: Papers that exhibit the highest quality in terms of
originality, rigor and significance will appear in a special issue
of IEEE Transactions on Visualization and Computer Graphics
(TVCG). The acceptance rate is anticipated to be similar to that of
past years (around 22%-25%), subject to the decisions resulting from
the review process. After initial notification of review results,
conditionally accepted papers (including supplemental material) will
undergo a revision and review cycle in order to ensure that they are
acceptable for publication and presentation in the journal. The
paper and supplemental material will also be submitted to the IEEE
Digital Library, subject to its standard terms and conditions.
Conference-only Track: Top quality and timely, innovative VAST
submissions may be accepted for the conference-only track. Those
papers, which feature new contributions, will be presented as
Conference Papers during IEEE VAST, and will be included on the IEEE
VIS USB Proceedings. After initial notification of review results,
conditionally accepted papers (including supplemental material) will
undergo a revision and approval cycle. The paper and supplemental
material will be submitted to the IEEE Digital Library subject to
its standard terms and conditions.
Visual analytics is the science of analytical reasoning supported by
highly interactive visual interfaces. People use visual analytics
tools and techniques in all aspects of science, engineering, business,
and government to synthesize information into knowledge; derive
insight from massive, dynamic, and often conflicting data; detect the
expected and discover the unexpected; provide timely, defensible, and
understandable assessments; and communicate assessments effectively
for action. The issues stimulating this body of research provide a
grand challenge in science: turning information overload into a
significant opportunity. Visual analytics requires interdisciplinary
science, going beyond traditional scientific and information
visualization to include statistics, mathematics, knowledge
representation, management and discovery technologies, cognitive and
perceptual sciences, decision sciences, and more. Your submission
should help develop and/or apply visual analytics, clearly showing an
From its outset, IEEE VAST has always given great emphasis to
applications of visual analytics. While VAST 2017 seeks submissions in
all areas of visual analytics, it particularly welcomes papers that
make advances towards understanding or solving real world problems, or
that impact a particular application in a significant way. A strong
application paper, for which technique novelty is not essential,
typically features one of the following qualities: namely high or
broad impact, novel application, innovative technical adaptation or
integration, or insightful experience or evaluation. For further
discussion of application papers, see the VAST paper types below.
Suggested topics for papers include, but are not limited to:
- Visual representations and interaction techniques including the
principles for depicting information, new visual paradigms,
statistical graphics, geospatial visualizations, the science of
interaction, and approaches for generating visual analytic
visualization and interactions.
- Data management and knowledge representation including scalable data
representations for high volume and stream data, statistical and
semantic signatures, and synthesis of information from diverse data
- Mathematical foundations and algorithms for data transformations to
allow interactive visual analysis.
- Analytical reasoning including the human analytic discourse,
knowledge discovery methods, perception and cognition, and
collaborative visual analytics
- Presentation, production, and dissemination methods including
methods and tools for capturing the analytics process, methods for
elicitation of stakeholder constraints, priorities & processes for
incorporation in analysis, and storytelling for specific and varying
- Applications of visual analysis techniques, including but not
limited to applications in science, engineering, humanities,
business, public safety, commerce, and logistics as far as they
contribute to visual analytics are of particular interest.
- Evaluation methods, visual ethical analysis such as privacy,
security, & regulatory compliance, interoperability, and technology
practice & experience.
- Discourse visualization and visual representations of the reasoning
- Algorithms and technologies which are fundamental for visual
analytics, including user and device adaptivity, web interfaces and
mobile or other novel devices.
Please note that topics primarily involving spatial data (such as
scalar, vector and tensor fields) might be a better match for SciVis:
the IEEE Conference on Scientific Visualization at IEEE
VIS. Similarly, topics which clearly focus on information
visualization, e.g., graphical representation of abstract data to aid
cognition, might be a better match for the IEEE InfoVis Conference,
also at IEEE VIS. Papers chairs reserve the right to move papers
between conferences based on its topic and perceived fit.
VAST has two tracks, TVCG and Conference-only tracks, which correspond
to different levels of originality, rigor, and significance. In
general, VAST papers should be written, submitted and reviewed in the
same way as papers at the other two VIS conferences (i.e., InfoVis and
SciVis), following the detailed submission guidelines. However, with
the rapid development of the science, technology and application of
visual analytics, it is sensible to adjust our understanding of VAST
publications from time to time. We provide the following
clarifications about paper types for VAST 2016, beyond the discussion
of the five paper types in the shared guidelines.
In visual analytics, concepts, theories, algorithms, techniques,
designs, systems, empirical studies and applications normally create a
context where analysis, visualization and interaction are integrated
to optimize the combination of human and machine capabilities. It is
this context that differentiates VAST from other conferences in VIS,
while data involved can be spatial or non-spatial, techniques can be
human-centric or machine-centric, and the application domain can be
almost any academic discipline, industry, business sector, or
governmental operation. Within such a context, an individual VAST
paper may give a strong focus on an aspect where novel contributions
reside, or place its emphasis on the integration of different aspects.
VAST papers typically fall into one of these six categories:
- Theory and Model
- Technique and Algorithm
- Design Study
- Empirical Study (referred to as the Evaluation type in in the shared guidelines)
- Application (as a separate category from the Design Study type in the shared guidelines)
Theory and Model
- Fundamentals of visual analytics.
- Conceptual understanding and modelling of visual analytics (e.g.,
definitions, taxonomies, analytic frameworks, and research methods,
- Philosophical and sociological discourses of visual analytics (e.g.,
human vs machine, ethics, data security, uncertainty, biases, and
- Perception and cognition in visual analytics.
- Mathematical abstraction and modelling of visual analytics processes.
- Concepts and models that govern quality metrics and benchmarks for
evaluating visual analytics processes and systems.
- F. Dabek and J. J. Caban, “A Grammar-based Approach for Modeling
User Interactions and Generating Suggestions During the Data
Exploration Process” in IEEE Transactions on Visualization and
Computer Graphics, vol. 23, no. 1, pp. 41-50, Jan. 2017. doi:
- G. K. L. Tam, V. Kothari and M. Chen, “An Analysis of Machine- and
Human-Analytics in Classification” in IEEE Transactions on
Visualization and Computer Graphics, vol. 23, no. 1, pp. 71-80,
Jan. 2017. doi: 10.1109/TVCG.2016.2598829. VAST 2016 Best Paper.
- M. Monroe, R. Lan, H. Lee, C. Plaisant and B. Shneiderman, “Temporal
Event Sequence Simplification” in IEEE Transactions on Visualization
and Computer Graphics, vol. 19, no. 12, pp. 2227-2236,
Dec. 2013. doi: 10.1109/TVCG.2013.200. VAST 2013 Honorable Mention.
Technique and Algorithm
- Visualization techniques in visual analytics processes.
- Close integration of technical components of visual analytics (e.g.,
statistical analysis, data mining and machine learning algorithms,
knowledge representations, visualization/interaction techniques and
methodologies, etc.) for supporting visual data mining.
- Visual analytics for supporting the advancement of non-visual
technical components of visual analytics (e.g., visual analytics for
supporting model selection and parameter setting, simulation,
clustering and classification, learning, prediction, monitoring, and
- Integrated data acquisition, management, retrieval, processing and
transformation in visual analytics (e.g., multi-sources;
multi-resolution; data provenance; uncertainty; real world measures;
textual, audio, visual and other media; factual, statistical,
semantic, synthesized, and hypothesized data; etc.).
- VA techniques for spatial and non-spatial data, temporal data,
streaming data, quantitative and qualitative data, text and document
data, model visualization, and so on.
- Techniques for production, presentation, and dissemination of VA results.
- C. Xie, W. Zhong and K. Mueller, “A Visual Analytics Approach for
Categorical Joint Distribution Reconstruction from Marginal
Projections” in IEEE Transactions on Visualization and Computer
Graphics, vol. 23, no. 1, pp. 51-60, Jan. 2017. doi: 10.1109/TVCG.2016.2598479. VAST 2016 Honorable Mention.
- S. van den Elzen, D. Holten, J. Blaas and J. J. van Wijk, “Reducing
Snapshots to Points: A Visual Analytics Approach to Dynamic Network
Exploration” in IEEE Transactions on Visualization and Computer
Graphics, vol. 22, no. 1, pp. 1-10, Jan. 31 2016. doi: 10.1109/TVCG.2015.2468078. VAST 2015 Best Paper.
- T. Mühlbacher and H. Piringer, “A Partition-Based Framework for
Building and Validating Regression Models” in IEEE Transactions on
Visualization and Computer Graphics, vol. 19, no. 12, pp. 1962-1971,
Dec. 2013. doi: 10.1109/TVCG.2013.125. VAST 2013 Best Paper.
- Understanding human-centric components in visual analytics processes
(e.g., perception, cognition, interaction, communication,
- Understanding human capabilities and limitations in data
intelligence (e.g., exploration, navigation, sensemaking, context
awareness, knowledge discovery, learning, argumentation, causality
reasoning, accountability, biases, etc.).
- Understanding visual signatures in data intelligence (e.g., patterns
of clusters, patterns of anomalies, etc.).
- Understanding the potential merits and demerits of technologies in
visual analytics (e.g., display technologies, interactive
technologies, automated analytics, crowdsourcing analytics, and so
- Human-centric comparative studies on aspects of visual analytics
(e.g., visual representations, interaction techniques, active
learning, visual analytics literacy, requirements analysis, etc.).
- Evaluation methodologies for visual analytics techniques and systems
in real world environments.
- Different quantitative and qualitative (including ethnographic)
forms of empirical studies (e.g., lab-based studies, field studies,
crowdsourcing, group discussions, surveys, interviews, user
experience observation, shadowing, case studies and casebook
- Transformation of scenarios and data captured in studies to
benchmark problems and data-driven metrics.
- A. Dasgupta et al., “Familiarity Vs Trust: A Comparative Study of
Domain Scientists’ Trust in Visual Analytics and Conventional
Analysis Methods” in IEEE Transactions on Visualization and Computer
Graphics, vol. 23, no. 1, pp. 271-280, Jan. 2017. doi: 10.1109/TVCG.2016.2598544.
- H. Guo, S. R. Gomez, C. Ziemkiewicz and D. H. Laidlaw, “A Case Study
Using Visualization Interaction Logs and Insight Metrics to
Understand How Analysts Arrive at Insights” in IEEE Transactions on
Visualization and Computer Graphics, vol. 22, no. 1, pp. 51-60,
Jan. 31 2016. doi: 10.1109/TVCG.2015.2467613. VAST 2015 Honorable
- N. Mahyar and M. Tory, “Supporting Communication and Coordination in
Collaborative Sensemaking” in IEEE Transactions on Visualization and
Computer Graphics, vol. 20, no. 12, pp. 1633-1642, Dec. 31 2014.
doi: 10.1109/TVCG.2014.2346573. VAST 2014 Best Paper.
- Designing disseminative visual analytics (e.g., storytelling,
illustration and animation, public engagement, etc.)
- Designing observational visual analytics (e.g., multivariate data,
streaming data, multimedia data, geospatial data, spatio-temporal,
- Designing analytical visual analytics (e.g., clustering, anomaly
detection, association and network analysis, correlation, causality,
- Designing model-developmental visual analytics (e.g., exploring
parameter space, and model space, supporting dimensionality
reduction and machine learning, model-developmental life cycle,
- Design methodologies for real world visual analytics systems and
- J. Zhao, N. Cao, Z. Wen, Y. Song, Y. R. Lin and C. Collins,
“#FluxFlow: Visual Analysis of Anomalous Information Spreading on
Social Media” in IEEE Transactions on Visualization and Computer
Graphics, vol. 20, no. 12, pp. 1773-1782, Dec. 31 2014. doi: 10.1109/TVCG.2014.2346922. VAST 2014 Honorable Mention.
- P. Isenberg, D. Fisher, M. R. Morris, K. Inkpen and M. Czerwinski,
“An exploratory study of co-located collaborative visual analytics
around a tabletop display” 2010 IEEE Symposium on Visual Analytics
Science and Technology, pp. 179-186. doi: 10.1109/VAST.2010.5652880. VAST 2010 Honorable Mention.
- E. A. Bier, S. K. Card and J. W. Bodnar, “Entity-based collaboration
tools for intelligence analysis” 2008 IEEE Symposium on Visual
Analytics Science and Technology, pp. 99-106. doi: 10.1109/VAST.2008.4677362. VAST 2008 Best Paper.
- Methodologies for engineering real world visual analytics systems
- System platforms (from wearable devices to desktops to large
infrastructures, and from architectures and software libraries
(toolkits), to stand alone systems and apps, to online services and
open source repositories).
- Comparative studies on real world visual analytics systems.
- Development tools for the software lifecycle of visual analytics
systems, including requirements analysis, system specification,
system design, system implementation, system testing, user
evaluation, and system maintenance).
- Addressing challenges in real world visual analytics systems (e.g.,
provenance management, scalability, uncertainty, open testbeds,
- Automation, customization, and personalization, and
- Best practices (e.g., interoperability, workflow design,
cost-benefit analysis, standardization, etc.)
- P. Xu, H. Mei, L. Ren and W. Chen, “ViDX: Visual Diagnostics of
Assembly Line Performance in Smart Factories” in IEEE Transactions
on Visualization and Computer Graphics, vol. 23, no. 1, pp. 291-300,
Jan. 2017. doi: 10.1109/TVCG.2016.2598664. VAST 2016 Honorable
- T. Blascheck, M. John, K. Kurzhals, S. Koch and T. Ertl, “VA2: A
Visual Analytics Approach for Evaluating Visual Analytics
Applications” in IEEE Transactions on Visualization and Computer
Graphics, vol. 22, no. 1, pp. 61-70, Jan. 31 2016. doi: 10.1109/TVCG.2015.2467871. VAST 2015 Honorable Mention.
- S. Koch, H. Bosch, M. Giereth and T. Ertl, “Iterative integration of
visual insights during patent search and analysis” 2009 IEEE
Symposium on Visual Analytics Science and Technology,
pp. 203-210. doi: 10.1109/VAST.2009.5333564. VAST 2009 Best Paper.
- Delivering visual analytics solutions to applications in academic
disciplines (e.g., physical sciences, biological and medical
sciences, engineering sciences, social sciences, arts and
humanities, and sports sciences).
- Delivering visual analytics solutions to applications in industries
- Delivering visual analytics solutions to applications in public
services and entertainment (e.g., resilience, healthcare, transport,
sports, tourism, broadcasting, and social media).
- D. Liu et al., “SmartAdP: Visual Analytics of Large-scale Taxi
Trajectories for Selecting Billboard Locations” in IEEE Transactions
on Visualization and Computer Graphics, vol. 23, no. 1, pp. 1-10,
Jan. 2017. doi: 10.1109/TVCG.2016.2598432.
- F. Beck, S. Koch and D. Weiskopf, “Visual Analysis and Dissemination
of Scientific Literature Collections with SurVis” in IEEE
Transactions on Visualization and Computer Graphics, vol. 22, no. 1,
pp. 180-189, Jan. 31 2016. doi: 10.1109/TVCG.2015.2467757.
- C. Shi, Y. Wu, S. Liu, H. Zhou and H. Qu, “LoyalTracker: Visualizing
Loyalty Dynamics in Search Engines” in IEEE Transactions on
Visualization and Computer Graphics, vol. 20, no. 12, pp. 1733-1742,
Dec. 31 2014. doi: 10.1109/TVCG.2014.2346912. VAST 2014 Honorable
Multi-type Papers. It is important to note that a VAST paper can present a mixture of contributions that fall into different categories. For example, a new technique may be presented in conjunction with an important application; a new design study may be led by an empirical study and supported by qualitative evaluation; a theoretical model may be supported by evidence from a real world application; and so forth. We encourage reviewers to appreciate the combined values of the mixed contributions rather than shoehorning such a paper into a specific category. Authors are encouraged to explicitly state the contributions made by their paper to all involved categories of research.
Authors’ Perspective. The VIS guidelines, together with cited papers and reports, provide authors, especially less experienced authors, with useful guidance to organize their research activities and structure papers. Even experienced authors should not overlook such guidelines.
Reviewers’ Perspective. Meanwhile, since VAST research usually features innovation, creativity and cross-disciplinarity, reviewers should not use these guidelines as a check list for acceptance or rejection in a simplistic manner. The goal of the review process is to bring the most exciting or important advances in areas of visual analytics to the VIS attendees, TVCG readers, and the larger community. Hence the role of a reviewer should be closer to a judge for a talent show than a hygiene inspector.
Reviewing is essentially an evaluation process with reviews intended to offer a balanced assessment of originality, rigor, and significance. For VAST, we particularly welcome papers that excel in at least one of these three aspects while being adequate in others. We equally welcome papers that feature significant impact on visual analytics applications, and/or interdisciplinary research activities (e.g., machine learning, cognitive sciences, and so on). Reviewers are encouraged to appraise positively Application papers and Empirical Study papers that feature such qualities.
- Brian Fisher, Simon Fraser University, Canada
- Shixia Liu, Tsinghua University, China
- Tobias Schreck, TU Graz, Austria