CFP Interactive Visual Analytics and Visualization for Decision-making
Due July 15
We are seeking submissions for the Interactive Visual Analytics and Visualization for Decision-making minitrack at the Hawaii International Conference for Systems Sciences. The conference is currently planned to be held Kauai on January 5-8, 2021, with alternative arrangements for those who cannot attend in person. We look forward to receiving your submissions by July 15: https://hicss.hawaii.edu/authors/
Accepted papers will be archived in the IEEE Digital Library. HICSS is among the most c Feel free to contact the mintrack chairs (see below) with any questions or concerns.
Interactive Visual Analytics and Visualization for Decision Making supports human decision making through interaction with data and statistical and machine learning processes, with applications in a broad range of situations where human expertise must be brought to bear on problems characterized by massive datasets and data that are uncertain in fact, relevance, location in space and position in time. Current applications include environmental science and technologies, natural resources and energy, health and related life sciences, precision medicine, safety and security and business processes.
Submissions are encouraged that extend the areas of use to new analytic tasks in science and technology, public health, business intelligence, financial analysis, social sciences, and other domains. Particular emphasis will be given to submissions that use visual analytics for social change discovery, analysis, communication, and focus on mixed initiative analysis recognizing that human and machine have a distinct division of labor in the problem-solving process. Submissions may include studies of visual analytics and decision support in the context of an organization (e.g., communication between analysts and policy-makers), perceptual and cognitive aspects of the analytic task, Interactive Machine Learning, and collaborative analysis using visual information systems. Additionally, submissions may include understandable, trustable AI as well as human-guided AI to round out the problem-solving process.
We encourage authors to address the following themes from their own research perspectives. Authors are encouraged to bring the lens of their own background and expertise to focus on the analytics of the data itself and coordination of multiple levels of analysis, decision-making and operations to the design and evaluation of effective presentations for stakeholders.
· Visual analytics and visualization in digital economies
· Visual analytics and visualization in “wicked” problems
· Visualization in organizational analytics
· Visualization and analysis of datasets of varying size and complexity from archives and real-time streams
· Collaborative visual analysis and operational coordination within and across organizations.
· Interactive and visual risk-based decision making
· Interactive machine learning methods
· Managing response time of complex analytical tasks
· Effective deployment and case studies of success from deployed visualization and analytics experiences
· Visualization and analytics for data-driven policy making and decision support
· Issues and challenges in evaluation of visual decision making
· Mixed-initiative analysis methods for decision making
· Cognitive and social science aspects of visual decision-making environments
For this minitrack, we invite computational, cognitive, and organizational perspectives on advanced data processing and interactive visualization across a range of human endeavors. We also invite participation from researchers who are looking at scaling issues and multiscale issues, whether these scales refer to the time of decision making, the form-factor and operational constraints of mobile devices, the number of decision makers or the more traditional notion of multiscale simulation and real-world scales of data. We are particularly interested in approaches that combine computational and interactive analytics in “mixed initiative” or Interactive Machine Learning systems, decision support in the context of an organization (e.g. communication between analysts and policy-makers), perceptual and cognitive aspects of the analytic task, and collaborative analysis using visual information systems.
David Ebert (Primary Contact)
Simon Fraser University
University of Texas at Austin