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Please consider submitting your paper  by June 15, to Interactive Visual Analytics and Visualization for Decision Making – Making Sense of Big Data
in the Decision Analytics, Mobile Services and Service Science Track at HICSS 51

Interactive Visual Analytics and Visualization for Decision Making – Making Sense of Big Data

Computational analysis of large and complex datasets is becoming increasingly common in many and diverse areas. In applications such as health care, business operations, impactful scientific research, and government operations human judgement about situations, contingency plans, compliance with law and policy,  and ethical implications must be informed by information contained in these large and complex datasets. Statistical analysis and machine learning processes play a key role in this, but for a broad range of situations human expertise must also 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.

Interactive Visual Analytics and Visualization for Decision Making bridges human decision making, large and complex datasets, and computational analysis. Applications include environmental science and technologies, natural resources and energy, health and related life sciences, 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 and communication. 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. Topics include:

    ï    Visualization and Analysis of datasets of varying size and complexity from archives and real-time streams
    ï    Support for collaborative analysis, planning, and operational coordination within and across organizations.
    ï    Support for risk-based decision making
    ï    Interactive Machine Learning methods
    ï    Cross-platform interoperability, from mobiles to data walls
    ï    Managing response time of complex analytical and operational tasks
    ï    Effective deployment and case studies of success from deployed visualization and analytics experiences
    ï    Social media and streaming data visual analytics
    ï    Visualization and analytics for data-driven policy making and decision support
    ï    Business intelligence, organizational, financial, and economic decision making visual analytics
    ï    Analysis and operational planning in health research and health care delivery
    ï    Issues and Challenges of evaluation of visual decision making
    ï    Cognitive and social science aspects of visual decision making environments

For HICSS 2018, we encourage authors to address these 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.  

Minitrack Chairs
David S. Ebert, Silicon Valley Professor of ECE, Director, DHS VACCINE Center of Excellence
School of Electrical and Computer Engineering, Purdue University
Email: ebertd@purdue.edu

Brian Fisher, Professor
School of Interactive Arts and Technology, Simon Fraser University
Email: bfisher@sfu.ca

Kelly Gaither, Director of Visualization, Interim Director of Education & Outreach, Senior Research Scientist
Texas Advanced Computing Center, University of Texas at Austin
Email: kelly@tacc.utexas.edu