Abstract-We present MoleView, a novel technique for interactive exploration of multivariate relational data. Given a spatial embedding of the data, in terms of a scatter plot or graph layout, we propose a semantic lens which selects a specific spatial and attribute-related data range. The lens keeps the selected data in focus unchanged and continuously deforms the data out of the selection range in order to maintain the context around the focus. Specific deformations include distance-based repulsion of scatter plot points, deforming straight-line node-link graph drawings, and as varying the simplification degree of bundled edge graph layouts. Using a brushing-based technique, we further show the applicability of our semantic lens for scenarios requiring a complex selection of the zones of interest. Our technique is simple to implement and provides real-time performance on large datasets. We demonstrate our technique with actual data from air and road traffic control, medical imaging, and software comprehension applications. Index Terms-Semantic lenses, magic lenses, graph bundling, attribute filtering. In recent years, the amount of data which information visualization techniques are confronted with has increased massively, whereas display sizes have remained largely identical. Infovis techniques address this challenge in two main ways. First, datasets are simplified by clustering or subsampling, so they deliver manageable data amounts with respect to available screen size. Secondly, mapping techniques maximize the amount of information displayed per screen space area, or information density. However effective, techniques of the second type create additional challenges to explorative user interaction. Consider the case of dense node-link layouts or multivariate datasets displayed as scatterplots or parallel coordinates. Such techniques create significant amounts of overlap between the drawn elements (points or edges). This simplifies the resulting visualization by reducing the number of perceived elements. However, overlaps make it harder to explore the dataset: in typical 2D visualizations, it is hard or even impossible to see what is hidden 'under' the front-most elements, even when using transparency. Hidden elements cannot be easily selected and/or brushed over without additional interaction effort. We have the situation of a compact visualization (desirable from the viewpoint of scalability and, optionally, clutter reduction) which is suboptimal for interactive exploration. Finally, there are use-cases when a given dataset may be best understood by using several layouts, one for each aspect being examined. Displaying a one layout in separate linked views of all layouts can be suboptimal as it increases the effort required from the user to correlate between the different views. In this paper, we present MoleView, a framework for interactive exploration of large element-based plots, which are sets of discrete data elements, each with several data and/or position (layout) attributes, which are visualized using a single, rather than multiple, views. Examples thereof are node-link layouts, (multidimensional) scatter plots, and images. Our contributions are as follows. First, we extend the well-know semantic lens with a range-based attribute filter to select a 'data layer' at a user-defined point, i.e. a set of data elements falling within the lens' position and attribute filter values. Instead of hiding the elements in the lens which fail passing the attribute filter, we use a dynamic re-layouting technique to smoothly push these away from the lens, or pull them back, hence the name of our technique. Second, we extend our data-driven deformation idea to explore bundled graphs. Given a bundled and unbundled version of the same graph, we use the MoleView to control the bundling strength and which edges get bundled at a certain location. In this way, users can explore bundled graphs (e.g. dig into a bundle to extract edges of interest based on attribute value) or, conversely, interactively simplify a given layout by bundling uninteresting edges. Finally, we extend the semantic lens concept for the task of exploring a dataset by the smooth animated interpolation between two completely different layouts of the same data, using as example the exploration of two-dimensional scalar images. Our technique has just a few parameters which are simple to control by end users, can be efficiently implemented to provide real-time interaction on large datasets, and can be easily incorporated in existing Infovis data exploration applications. The structure of this paper is as follows. Section 1 presents related work in the area of semantic lenses for attribute-based exploration. Section 2 describes the principle of the MoleView technique and its three different modes on utilization (elements, bundles, and dual-layout), and illustrates our technique in practice on a range of real-world datasets. Section 3 discusses the presented technique. Finally, Section 4 concludes the paper with future work directions. Related work in Infovis falls within several areas, as follows. Magic lenses: The Magic Lens introduced the idea of locally modifying a screen region based on a user-selected operator [2] . Originally used for modifying the graphics appearance and/or editing the properties of shapes at a focal point, the Magic Lens was subsequently extended to allow more complex operations such as complex effect compositing and interactive lens parameter editing [1] . Tangible magic lenses extended the base concept to allow users to 'slice' through, or zoom in, layered 2D or 3D datasets by interactively moving a 3D tracked physical planar object (the lens) which is either rigid [24] or flexible [15] . Nonlinear projection was added to magic lenses to deform 3D scenes as if seen through a cylindrical or spherical lens, working fully in image space, i.e. without access to the actual 3D scene [34] . The dust and magnet technique allows users to de-clutter large scattered plots by placing several data-attribute-driven 'magnets' in the display space and moving data points close to them based on the points' attributes [35] . This metaphor is somewhat similar to the preset controller [29] which is, however, used for the inverse operation of synthesizing data values based on the distance of a cursor to several data-attributed presets. The bundled graph visualization presented 2600 1077-2626/11/$26.00
The MoleView technique deforms data based on both spatial position and data values, allowing to 'dig' in hidden data layers REF .
MoleView: An Attribute and Structure-Based Semantic Lens for Large Element-Based Plots
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics
Computer Science Medicine