Analysts need to keep track of their analytic findings, observations, ideas, and hypotheses throughout the analysis process. While some visual analytics tools support such note-taking needs, these notes are often represented as objects separate from the data and in a workspace separate from the data visualizations. Representing notes the same way as the data and integrating them with data visualizations can enable analysts to build a more cohesive picture of the analytical process. We created a note-taking functionality called CZNotes within the visual analytics tool CZSaw for analyzing unstructured text documents. CZNotes are designed to use the same model as the data and can thus be visualized in CZSaw's existing data views. We conducted a preliminary case study to observe the use of CZNotes and observed that CZNotes has the potential to support progressive analysis, to act as a shortcut to the data, and supports creation of new data relationships.

Investigative analysts need overviews of large amounts of data, which is a challenge when working with non-numerical data such as document collections. We present Semantic Zoom View (SZV), an interactive document collection visualization implemented as part of the CZSaw visual analytics system. SZV uses a focus + context technique to provide an overview with details on demand through interactive semantic zooming. SZV lets an analyst easily and quickly see the main topics of a document collection while keeping surrounding documents visible for context. Working within a single integrated visualization, an analyst can also quickly find related documents and break a large document collection into smaller meaningful groups. SZV's focus + context technique was compared to an overview + detail version for finding answers within a document collection and results indicated its strength for maintaining visibility of a full overview when document contents are accessed.

CZSaw [1] is a visual analytics tool for sense-maki ng across entities, entity collections, and relations with a focus on augmenting the analysis process. It uses a variety of flexible data visualizations to represent, explore, and compute n etworks of entities and relations from different perspectives. CZSaw is designed to provide a replayable record of the anal ysis process and to generate a reusable model of the analysis lo g c, structured as a dependency graph. To support these goals, sema ntically meaningful interactions are captured into a script. Replaying this script replays the analysis process, and editing it allows fine control and reuse of the process. Specialized viewe rs are also provided for the dependency graph and for the user’ s history, to provide more visual interaction. This demo shows ho w CZSaw can be used to analyze different types of datasets ( tructured and unstructured data), as well as some strategies (e.g . divide and conquer) used on analysis tasks.

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). 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.