This paper points out the potential research directions for visual analytics.
- let user obtain deep insight, assessment, planning and decision making.
- let user see, explore and understand large amounts of information simultaneously
- convert all types of conflicting and dynamic data in ways that support visualization and analysis.
- communicate the information in the appropriate context to a variety of audiences.
The science of analytical reasoning, take a crisis event as example.
- understanding historical and current situations.
- identifying possible alternative future scenarios
- monitoring current events to identify both expected and unexpected events.
- determining indicators of the intent of an action or an individual.
- support the decision maker in times of crisis.
visual representations and interaction technologies
- facilitate understanding of massive and continually growing collections of data of multiple types.
- provide frameworks for analyzing spatial and temporal data
- support the understanding of uncertain, incomplete, and misleading information.
- provide user and task-adaptable guided representations that enable full situation awareness while supporting development of detailed actions.
- support multiple levels of data and information abstraction, including integration of different types of information into a single representation.
Data representations and transformations
- transforming data into new scalable representations that faithfully represent the underlying data's relevant content.
- synthesize different types of information from different sources into a unified data representation, so users can focus on the data's meaning in the context of other relevant data
- develop methods and principles for representing data quality, reliability and certainty, measure through-out the data transformation and analysis process.
Reference
- Thomas, James J., and Kristin A. Cook. "A visual analytics agenda." IEEE computer graphics and applications 26.1 (2006): 10-13.
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