Abstract: Recently, to address the challenges imposed by big data, research on interactive
visualization and visual analytics, which is aimed at combining human intelligence and machine
computational powers in data analysis, has become increasingly important. However, the design of
complex-information-oriented interactive visualization and visual analytics systems still lacks
effective cognitive foundations as a guidance. Existing frameworks or design guidelines, such as
sense-making, focus largely on the external characteristics of analytical activities and offer little
insight into fundamental cognition that underpins analytical behaviors. In this paper, we proposed a
theoretical framework based on problem-solving theories to help explain the essential cognitive
activities in interactive visual analytics, advocated an approach to understanding the visual analytics process as a process to solve ill-defined problems, and explored the impacts of problem
representations and problem-solving strategies on visual analytics behaviors. We see our contribution
as two-folded. Theoretically, we borrowed problem-solving theories from cognitive science to study
complex interactive activities in visual analytics, and this approach may offer insight into the design
of interactive systems involving complex information-analytical behaviors. Practically, we discussed
the design and evaluation of visual analytics tools from the perspective of supporting problem-solving.

Key words: problem-solving, visual analytics, sense-making, problem representation, problem-solving
strategy