EuroVA 2016 - 2016 EuroVis Workshop on Visual Analytics

Visual Analytics has been defined as the science of analytical reasoning facilitated by interactive visual interfaces. Analytical reasoning includes analysis of complex (massive, dynamic, ambiguous, conflicting, …) data and information for gaining understanding, building knowledge, solving complex problems, and making right decisions. Visual Analytics deals with problems that cannot (yet) be solved algorithmically and therefore essentially require human thinking supported by the power of computers. Visual Analytics aims at effective division of labour between humans and computers and their synergistic collaboration, in which each side can efficiently utilize its unique capability.
Thus, Visual Analytics can be understood as the science of human-computer data analysis, knowledge building, and problem solving. It is an interdisciplinary science integrating techniques from visualization and computer graphics, statistics and mathematics, data management and knowledge representation, data analysis and machine learning, cognitive and perceptual sciences, and more.
EuroVA 2016 is the best place to present and discuss ideas of new methods and theories, interesting applications, designs, and studies of the use of visual analytics methods and systems. The workshop will accept a range of paper types, including technique, system, application, evaluation and theory papers in the area of Visual Analytics.
TOPICS of interest include but are not limited to:
Combining interactive visualization with computational techniques from statistics, data mining, machine learning or similar.
Visual representations and interactive techniques amplifying human analytical reasoning.
Visual analytics support to decision making.
Modelling of uncertainty and supporting uncertainty-aware analytical reasoning.
Visual analysis procedures, processes, and workflows.
Visual data science and predictive visual analytics.
Visualization of models derived by visual analytics methods and procedures.
Visual explorations and analysis of (e.g., simulation) models and their parameter spaces
Visual analytics data provenance, and management of analytic processes.
Data/information management and representation for visual analytics methods.
Theoretical foundations of visual analytics.
Cognitive and perceptual aspects of visual analytics.
Theories and models of analytical reasoning, human analytic discourse, sensemaking.
Collaborative visual analytics.
Infrastructures and architectures for visual analytics systems and services.
Visual analytics applications.
Evaluation of visual analytics techniques and procedures.