  Interactive visual analytic systems enable users to discover insights from
complex data. Users can express and test hypotheses via user interaction,
leveraging their domain expertise and prior knowledge to guide and steer the
analytic models in the system. For example, semantic interaction techniques
enable systems to learn from the user's interactions and steer the underlying
analytic models based on the user's analytical reasoning. However, an open
challenge is how to not only steer models based on the dimensions or features
of the data, but how to add dimensions or attributes to the data based on the
domain expertise of the user. In this paper, we present a technique for
inferring and appending dimensions onto the dataset based on the prior
expertise of the user expressed via user interactions. Our technique enables
users to directly manipulate a spatial organization of data, from which both
the dimensions of the data are weighted, and also dimensions created to
represent the prior knowledge the user brings to the system. We describe this
technique and demonstrate its utility via a use case.
