Timeline visualization is an important tool for sense making. It allows analysts to examine information in chronological order and to identify temporal patterns and relationships. However, many existing timeline visualization methods are not designed for the dynamic and iterative nature of the sense making process and the various analysis activities it involves. In this paper, we introduce a novel timeline visualization, Schema Line, to address these deficiencies. Schema Line is designed to group notes into analyst-determined schema, using a layout algorithm to produce compact but aesthetically pleasing timeline visualization, and includes fluid user interactions to support sense making activities. It enables interactive temporal schemata construction with seamless integration with visual data exploration and note taking. Our preliminary evaluation results show that the participants found the new method easy to learn and use, and its features effective for the sense making activities for which it was designed.

Parallel coordinates are a well-known and valuable technique for the analysis and visualization of high dimen-sional data sets. However, while Inselberg emphasizes that the strength of parallel coordinates as a methodology is rooted in exploration and interactivity, the set of interac-tion techniques is currently limited. Axes can be re-ordered and brushing (simple, angular or multi-dimensional) can be performed. In this paper, we propose a force-directed algorithm and related interaction techniques to support the exploration of parallel coordinate plots through a physi-cal metaphor. Our parallel-coordinates visualization of-fers novel user interaction beyond the standard techniques by allowing the user to rotate the axis according to force-directed polylines. The new interaction provides the user with a more immersive experience for data exploration that results in greater intuition of the data, especially in cases where many polylines overlap. We demonstrate our ap-proach, then present the results of a qualitative evaluation of the system. 1

We describe and demonstrate an extensible framework that supports data exploration and provenance in the context of Human Terrain Analysis (HTA). Working closely with defence analysts we extract requirements and a list of features that characterise data analysed at the end of the HTA chain. From these, we select an appropriate non-classified data source with analogous features, and model it as a set of facets. We develop ProveML, an XML-based extension of the Open Provenance Model, using these facets and augment it with the structures necessary to record the provenance of data, analytical process and interpretations. Through an iterative process, we develop and refine a prototype system for Human Terrain Visual Analytics (HTVA), and demonstrate means of storing, browsing and recalling analytical provenance and process through analytic bookmarks in ProveML. We show how these bookmarks can be combined to form narratives that link back to the live data. Throughout the process, we demonstrate that through structured workshops, rapid prototyping and structured communication with intelligence analysts we are able to establish requirements, and design schema, techniques and tools that meet the requirements of the intelligence community. We use the needs and reactions of defence analysts in defining and steering the methods to validate the framework.

Abstract Close collaboration with other scientific fields is an important goal for the visualization community. Yet engaging in a scientific collaboration can be challenging. The physical sciences, namely astronomy, chemistry, earth sciences and physics, exhibit an extensive range of research directions, providing exciting challenges for visualization scientists and creating ample possibilities for collaboration. We present the first survey of its kind that provides a comprehensive view of existing work on visualization for the physical sciences. We introduce novel classification schemes based on application area, data dimensionality and main challenge addressed, and apply these classifications to each contribution from the literature. Our survey helps in understanding the status of current research and serves as a useful starting point for those interested in visualization for the physical sciences.

Parallel coordinates is a popular and well-known multivariate data visualization technique. However, one of their inherent limitations has to do with the rendering of very large data sets. This often causes an overplotting problem and the goal of the visual information seeking mantra is hampered because of a cluttered overview and non-interactive update rates. In this paper, we propose two novel solutions, namely, angular histograms and attribute curves. These techniques are frequency-based approaches to large, high-dimensional data visualization. They are able to convey both the density of underlying polylines and their slopes. Angular histogram and attribute curves offer an intuitive way for the user to explore the clustering, linear correlations and outliers in large data sets without the over-plotting and clutter problems associated with traditional parallel coordinates. We demonstrate the results on a wide variety of data sets including real-world, high-dimensional biological data. Finally, we compare our methods with the other popular frequency-based algorithms.