Abstract — While there have been advances in visualization systems, particularly in multi-view visualizations and visual exploration, the process of building visualizations remains a major bottleneck in data exploration. We show that provenance metadata collected during the creation of pipelines can be reused to suggest similar content in related visualizations and guide semi-automated changes. We introduce the idea of query-by-example in the context of an ensemble of visualizations, and the use of analogies as first-class operations in a system to guide scalable interactions. We describe an implementation of these techniques in VisTrails, a publicly-available, open-source system. Index Terms—visualization systems, query-by-example, analogy 1

Abstract—Interactive history tools, ranging from basic undo and redo to branching timelines of user actions, facilitate iterative forms of interaction. In this paper, we investigate the design of history mechanisms for information visualization. We present a design space analysis of both architectural and interface issues, identifying design decisions and associated trade-offs. Based on this analysis, we contribute a design study of graphical history tools for Tableau, a database visualization system. These tools record and visualize interaction histories, support data analysis and communication of findings, and contribute novel mechanisms for presenting, managing, and exporting histories. Furthermore, we have analyzed aggregated collections of history sessions to evaluate Tableau usage. We describe additional tools for analyzing users ’ history logs and how they have been applied to study usage patterns in Tableau. Index Terms—Visualization, history, undo, analysis, presentation, evaluation. 1

To design information visualization tools for collaborative use, we need to understand how teams engage with visualizations during their information analysis process. We report on an exploratory study of individuals, pairs, and triples engaged in information analysis tasks using paper-based visualizations. From our study results, we derive a framework that captures the analysis activities of co-located teams and individuals. Comparing this framework with existing models of the information analysis process suggests that information visualization tools may benefit from providing a flexible temporal flow of analysis actions.

With visual analytical tools becoming more sophisticated and prevalent in the analysis communities, it is now apparent that understanding how analysts utilize these tools is more important than ever. Such understanding can lead to improving the tools, but a more subtle and equally important aspect lies in the discovery of the analysts ’ reasoning process for solving complex problems through the use of these visual analytical tools. In this paper we demonstrate that it is possible to extract strategies, methods, and findings of an analysis process through only the examination of an analyst’s interaction log. In our study, we record the interactions of 10 financial analysts in a fraud detection task. By examining their interactions, we are able to quantitatively show that up to 60.10% of strategies, 60.08 % of methods, and 79.03 % of findings can be recovered through the use of a strategic and an operational visual analytical tool. 1

Abstract—Building visualization and analysis pipelines is a large hurdle in the adoption of visualization and workflow systems by domain scientists. In this paper, we propose techniques to help users construct pipelines by consensus—automatically suggesting completions based on a database of previously created pipelines. In particular, we compute correspondences between existing pipeline subgraphs from the database, and use these to predict sets of likely pipeline additions to a given partial pipeline. By presenting these predictions in a carefully designed interface, users can create visualizations and other data products more efficiently because they can augment their normal work patterns with the suggested completions. We present an implementation of our technique in a publicly-available, open-source scientific workflow system and demonstrate efficiency gains in real-world situations.

Visual analytics tools provide powerful visual representations in order to support the sense-making process. In this process, analysts typically iterate through sequences of steps many times, varying parameters each time. Few visual analytics tools support this process well, nor do they provide support for visualizing and understanding the analysis process itself. To help analysts understand, explore, reference, and reuse their analysis process, we present a visual analytics system named CzSaw (See-Saw) that provides an editable and re-playable history navigation channel in addition to multiple visual representations of document collections and the entities within them (in a manner inspired by Jigsaw [24]). Conventional history navigation tools range from basic undo and redo to branching timelines of user actions. In CzSaw’s approach to this, first, user interactions are translated into a script language that drives the underlying scripting-driven propagation system. The latter allows analysts to edit analysis steps, and ultimately to program them. Second, on this base, we build both a history view showing progress and alternative paths, and a dependency graph showing the underlying logic of the analysis and dependency relations among the results of each step. These tools result in a visual model of the sense-making process, providing a way for analysts to visualize their analysis process, to reinterpret the problem, explore alternative paths, extract analysis patterns from existing history, and reuse them with other related analyses.

Abstract—To design information visualization tools that support users ’ needs, we need to understand how users engage with information visualizations in their analysis process. With the rapid growth in size and complexity of datasets, the practicality of an individual analyzing an entire dataset is becoming unrealistic. Instead, the expertise to make informed decisions about these information-rich datasets is often best accomplished by a team. However, there exist relatively few models that describe the visual analysis process, and only few studies that explore the differences between how individuals and teams use visualizations. We present an observational study where we explored the information analysis process of groups and individuals in the context of visual information. From the analysis of our study, we derive a framework that captures the activities of co-located teams and individuals engaged in information analysis. This framework has implications for the design, heuristic evaluation, and analysis of both collaborative and single-user digital information visualization tools.

reProducibility and beyond

Numerous scientific and analysis problems involve integrating heterogeneous information that is multidimensional, multivariate, and time-dependent. To visualize this data in a 3D environment, we designed a visualization technique called multiple scale small multiples (MSSM), in which each view behaves like an environment in itself. Aggregated multiple views can also form single view visualization. Data from bat flights and flow simulations were used to demonstrate MSSM at work. Users can interact with the dataset at different levels of detail and scale, and access a fairly large amount of data points. 1

A scalable supervised algorithm