![]() ![]() In parallel, there have been rapid advances in many facets of interaction technologies. This interplay, combined with the demands from increasingly large and complex datasets, is driving the increased significance of interaction in InfoVis. The importance of interaction to Information Visualization (InfoVis) and, in particular, of the interplay between interactivity and cognition is widely recognized. ![]() All of this work has led us to the draft of a new interaction paradigm, "the structural interaction", which extends the paradigms of direct manipulation and instrumental interaction. We also present the results of evaluations conducted with users in order to measure interest in the concepts provided by the tools developed. We present four interactive tools to illustrate concepts of interaction with structures: links delegation, which are based on the delegation mechanism in prototype languages, allowing to establish dependencies between objects (and create clones) and extend the scope of interactions by propagating them ManySpector, a new type of property inspector that reveals an implicit structuring of a scene and allows for building, with instrumental interactions, graphical query and selection IHR, a tool that can automatically create a hierarchy of delegated properties and Histoglass a movable lens that allows to locally manipulate the properties and past user actions. From contextual inquiries, we describe an analysis of needs which established a set of requirements for interactions on sets. Our thesis is that it is important to design interactions for the synchronous operation of multiple objects that are efficient, while fostering exploratory design. In this paper, we describe our techniques in three areas: 1) the design space of sketching methods for eliciting users' nonlinear domain knowledge 2) the underlying model that translates users' input, extracts patterns behind the selected data points, and results in nonlinear axes reflecting users' complex intent and 3) the interactive visualization for viewing, assessing, and reconstructing the newly formed, nonlinear axes.Īlthough many interactive tools allows manipulating effectively objects individually by applying principles such as direct manipulation, interaction with multiple objects have been little studied so far. Using this technique, users can draw lines over selected data points, and the system forms the axes that represent a nonlinear, weighted combination of multidimensional attributes. The proposed interaction is performed through directly sketching lines over the visualization. This technique enables users to impose their domain knowledge on a visualization by allowing interaction with data entries rather than with data attributes. To overcome these challenges, we introduce a technique to interpret a user's drawings with an interactive, nonlinear axis mapping approach called AxiSketcher. Furthermore, users' complex, high-level domain knowledge, compared to low-level attributes, posits even greater challenges. ![]() However, it is often challenging for users to express their domain knowledge in order to steer the underlying data model, especially when they have little attribute-level knowledge. Visual analytics techniques help users explore high-dimensional data. Finally, we present the results of two user studies, providing initial evidence that EMS can significantly reduce interaction time compared to WIMP-based technique and was subjectively preferred by participants. We also offer a set of design guidelines for supporting EMS. We demonstrate how the EMS technique can be designed to directly manipulate width and position in bar charts and histograms, as a means for adjustment of data grouping criteria. In response, we introduce Embedded Merge & Split (EMS), a new interaction technique for direct adjustment of data grouping criteria. Such adjustments are currently performed either programmatically or through menus and dialogues which require specific parameter adjustments over several steps. Although grouping plays a pivotal role in supporting data exploration, further adjustment and customization of auto-generated grouping criteria is non-trivial. ![]() Many popular visualization tools support automatic grouping of data (e.g., dividing up a numerical variable into bins). It is the process through which relevant information is gathered, simplified, and expressed in summary form. Data grouping is among the most frequently used operations in data visualization. ![]()
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