Kris Cook, Georges Grinstein, Mark Whiting
The Visual Analytics Science and Technology (VAST) Challenge is an annual contest with the goal of advancing the field of visual analytics through competition. The VAST Challenge is designed to help researchers understand how their software would be used in a novel analytic task and determine if their data transformations, visualizations, and interactions would be beneficial for particular analytic tasks. VAST Challenge problems provide researchers with realistic tasks and data sets for evaluating their software, as well as an opportunity to advance the field by solving more complex problems. This year’s VAST Challenge presents two related mini-challenges and an overall Grand Challenge, and we encouraged participants to create innovative visualizations to support their analyses of the data to solve a mystery.
The VAST Challenge workshop brings together organizers, participants, and conference attendees to discuss the innovative submissions to this year’s challenge. The workshop will feature sessions dedicated to each of the mini-challenges and the grand challenge. The 2015 award winners and honorable mention winners will present their submissions. In addition, the meeting will feature a poster session and a participant feedback session. This workshop is open to all IEEE VIS attendees.
Theresia Gschwandtner, Adam Perer, Jürgen Bernard
As medical organizations move to electronic medical records and increasingly embrace health information technology, the amount of data available is growing at an unprecedented rate. This vast amount of healthcare data poses a challenging task (1) for medical experts trying to make sense of patients’ conditions and understanding their medical history, (2) for patients trying to make sense of their health data, and (3) for analysts to conduct outcome research, such as exploring the effectiveness of different approaches. Visual Analytics and Information Visualization have the potential to provide great benefits to healthcare providers, patients, and data analysts. Given the strong turnout of this workshop in previous years, we propose to host a follow-up workshop at IEEE VIS 2015. In this workshop participants will have the opportunity to present ongoing work with short papers and demonstrations, and discuss user needs and challenges.
Charles Perin, Alice Thudt, Melanie Tory, Wesley Willett, Sheelagh Carpendale
Individuals recently began to seek how they can explore and understand the data that affect their personal lives. This includes biometric personal data such as health-related data, self-monitoring, sports performance, and data from online social networks, energy consumption, and photo collections. The main purpose of such data understanding is generating insights, and eventually making decisions to improve one's life, the ultimate purpose of visualization. Assessing individual's needs in the context of their personal data and designing appropriate tools to support visualization and analysis of this data is a crucial and emergent challenge. This workshop is intended to gather academics and industries concerned by the emergent topic of personal visualization and personal visual analytics. The intended outcomes of the workshop are 1) to gather the community working on the topic of personal visualization, and 2) to converge on a research agenda for the community.
Rahul Basole, Steven Drucker, Jörn Kohlhammer, Jarke Van Wijk
Companies of all sizes (startups to incumbents), shapes (public, private, non-profit), and industries (manufacturing, energy, healthcare, finance, technology, education, tourism) are inundated by an accelerating tsunami of relevant business data. Converting these diverse and heterogenous data into actionable insights and better business outcomes is a pressing and strategic challenge for all managers and decision makers. Despite the potential of visualization, existing applications are often limited to corporate dashboards. The real value is still untapped. With the growing prevalence of business analytics, what is the future of visualization in an increasingly data-driven business environment? How can visualizations be used to drive and augment business decisions? How do we bridge the gap between visualization research and practice? This half-day workshop will build on the momentum of the highly successful business|vis|14 workshop and aims to explore these questions. It will bring together researchers and practitioners interested in the design, development, and application of visualization and visual analytics to complex business problems. It will provide a fantastic opportunity for those engaged in this broad application domain to interact and share their experiences. Hopefully, it will spur a growing, focused sub¬area of data visualization for the future.
Daniel Weiskopf, Michael Burch, Albrecht Schmidt, Brian Fisher, Lewis Chuang
There is a growing interest in eye tracking as a research method in many communities, including information visualization, scientific visualization, visual analytics, but also in human-computer interaction, applied perception, psychology, cognitive science, security, and mixed reality. Progress in hardware technology and the reduction of costs for eye tracking devices have made this analysis technique accessible to a large population of researchers. Recording the observer's gaze can reveal how dynamic graphical displays are visually accessed and which information are processed in real time. Nonetheless, standardized practices for technical implementations and data interpretation remain unresolved. With this Workshop on Eye Tracking and Visualization (ETVIS), we intend to build a community of eye tracking researchers within the visualization community, covering information visualization, scientific visualization, and visual analytics. We also aim to establish connections to related fields, in particular, in human-computer interaction, cognitive science, and psychology. This will promote a robust exchange of established practices and innovative use scenarios.
Kristin Potter, Ruediger Westermann, Christoph Heinzl, Mike Kirby, Ross Whitaker, Eduard Groller, Torsten Moller, Stefan Bruckner
The goal of this workshop is to call on the research community to discuss the state-of-the-art and research challenges for supporting modeling and decision making under uncertainty in the computational and data sciences. When creating visual tools for simulations, challenges exist in the uncertainty analysis (UA) of ensembles, the sensitivity analysis (SA) of input-output models, and the decision making process that requires the understanding of risk stemming from both UA and SA.
Over the last few years we have seen many different attempts to address these issues, and it is now time to review the achievements in the light of real-world applications. We therefore attempt to broaden the focus of uncertainty analysis to a more comprehensive approach to modeling and discuss the current and future requirements from an application-oriented perspective.
The workshop shall bring together researchers from visualization and scientific domains where uncertainty - whether it is model-based uncertainty or data-based uncertainty - needs to be analyzed to enable an improved predictability of relevant events as well as their sensitivity to specific input model parameterizations.
Pak Chung Wong, David Haglin, David Bader, David Trimm
The workshop will explore the technical challenges and technology development opportunities of graph visual analytics found in the big data era with the goal of establishing a community of interest. Today’s graph problems are increasingly multi-faceted and multi-disciplinary in nature. Many cutting-edge R&D efforts are conducted independently in disparate domains such as bioinformatics, cybersecurity, and predictive machine learning. Although technology transfers in big graph visualization are recognized and growing, there has been little progress in establishing a community strategy for sharing and building knowledge.
We invite researchers and practitioners with different interests to participate at the workshop by submitting position papers and, if accepted, presenting their ideas at the workshop co-located at IEEE VIS 2015. We agree that the data size that seems big today is different from what seemed big only a few years ago. While the workshop doesn’t specify upper or lower bounds on the graph’s size, we are particularly interested in emerging problems that challenge conventional wisdom in computation and interaction brought by the latest social-scale or web-scale graphs. This workshop is organized by a group of big graph analytics researchers and practitioners who share a common goal of establishing a substantial community to solve big problems with big graph data.
Remco Chang, Danyel Fisher, Jeffrey Heer, Carlos Scheidegger
The goal of this workshop is to foster innovative research at the intersection of databases, machine learning, and interactive visualization. Database researchers have developed techniques for storing and querying massive amounts of data, including methods for distributed, streaming and approximate computation. Machine learning techniques provide ways to discover unexpected patterns and to automate and scale well-defined analysis procedures. Recent systems research has looked at how to develop novel database systems architectures to support the iterative, optimization-oriented workloads of machine learning algorithms.
Of course, both the inputs and outputs of these systems are ultimately driven by people, in support of analysis tasks. The life-cycle of data involves an iterative, interactive process of determining which questions to ask, the data to analyze, appropriate features and models, and interpreting results. In order to achieve better analysis outcomes, data processing systems require improved interfaces that account for the strengths and limitations of human perception and cognition. Meanwhile, to keep up with the rising tide of data, interactive visualization tools need to integrate more techniques from databases and machine learning.
In this workshop, we will explore the idea that the next-generation of database, machine learning, and interactive visualization systems should not be designed in isolation. For example, machine learning techniques might recommend improved data transformation and visual encoding decisions. Or, database query optimizers might take advantage of perceptual constraints, while prefetching methods reduce latency by modeling likely interactions.
This workshop seeks to jump start cross-pollination between these fields. The program will be split between invited talks from researchers in these communities, and speculative, ongoing work that straddles the areas. In addition, we will host a second session, off-site from the main VIS conference, where we will hold focused working groups for interested participants.