Lauren Bradel

Co-located collaborative sensemaking on a large high-resolution display with multiple input devices

This study adapts existing tools (Jigsaw and a text editor) to support multiple input devices, which were then used in a co-located collaborative intelligence analysis study conducted on a large, high-resolution display. Exploring the sensemaking process and user roles in pairs of analysts, the two-hour study used a fictional data set composed of 50 short textual documents that contained a terrorist plot and subject pairs who had experience working together. The large display facilitated the paired sensemaking process, allowing teams to spatially arrange information and conduct individual work as needed. We discuss how the space and the tools affected the approach to the analysis, how the teams collaborated, and the user roles that developed. Using these findings, we suggest design guidelines for future co-located collaborative tools.

Multi-model semantic interaction for text analytics

Semantic interaction offers an intuitive communication mechanism between human users and complex statistical models. By shielding the users from manipulating model parameters, they focus instead on directly manipulating the spatialization, thus remaining in their cognitive zone. However, this technique is not inherently scalable past hundreds of text documents. To remedy this, we present the concept of multi-model semantic interaction, where semantic interactions can be used to steer multiple models at multiple levels of data scale, enabling users to tackle larger data problems. We also present an updated visualization pipeline model for generalized multi-model semantic interaction. To demonstrate multi-model semantic interaction, we introduce StarSPIRE, a visual text analytics prototype that transforms user interactions on documents into both small-scale display layout updates as well as large-scale relevancy-based document selection.

Bixplorer: Visual Analytics with Biclusters

A prototype visual analytics tool uses data mining algorithms to find patterns in textual datasets and then supports exploration of these patterns in the form of biclusters on a high-resolution display.

The role of interactive biclusters in sensemaking

Visual exploration of relationships within large, textual datasets is an important aid for human sensemaking. By understanding computed, structural relationships between entities of different types (e.g., people and locations), users can leverage domain expertise and intuition to determine the importance and relevance of these relationships for tasks, such as intelligence analysis. Biclusters are a potentially desirable method to facilitate this, because they reveal coordinated relationships that can represent meaningful relationships. Bixplorer, a visual analytics prototype, supports interactive exploration of textual datasets in a spatial workspace with biclusters. In this paper, we present results of a study that analyzes how users interact with biclusters to solve an intelligence analysis problem using Bixplorer. We found that biclusters played four principal roles in the analytical process: an effective starting point for analysis, a revealer of two levels of connections, an indicator of potentially important entities, and a useful label for clusters of organized information.

Supporting the cyber analytic process using visual history on large displays

Cyber analytics focuses on increasing the safety and soundness of our digital infrastructure. The volume, size and velocity of these datasets make the analysis challenging on current work environments and tools. A cyber analytics work environment should enable multiple, simultaneous investigations and information foraging, as well as provide a solution space for organizing data. As such, various workflow visualization tools are used to help users track their analysis, reuse effective workflows, and test hypotheses. Also, the use of large display workspaces can provide new opportunities for improving visual analytics in cyber security. In this work, we present a prototype workspace for analysts where the analytic process is maintained in the workspace. Thus, we are able to present analysts with visual states of their data throughout the investigation, in which real-time changes can be made to any previous state, and analysts can backtrack through their investigation.

Big Text Visual Analytics in Sensemaking

Learning from text data often involves a loop of tasks that iterate between foraging for information and synthesizing it in incremental hypotheses. Past research has shown the advantages of using spatial workspaces as a means for synthesizing information through externalizing hypotheses and creating spatial schemas. However, spatializing the entirety of datasets becomes prohibitive as the number of documents available to the analysts grows, particularly when only a small subset are relevant to the tasks at hand. To address this issue, we applied the multi-model semantic interaction (MSI) technique, which leverages user interactions to aid in the display layout (as was seen in previous semantic interaction work), forage for new, relevant documents as implied by the interactions, and place them in context of the users existing spatial layout. Thus, this approach cleanly embeds visual analytics of big text collections directly into the human sensemaking process.

Size matters: the effects of interactive display size on interaction zone expectations

The goal of our research was to understand the effects of display size on interaction zones as it applies to interactive systems. Interaction zone models for interactive displays are often static and do not consider the size of the display in their definition. As the interactive display ecosystem becomes more size diverse, current models for interaction are limited in their applicability. This paper describes the results of an exploratory study in which participants interacted with and discussed expectations with interactive displays ranging from personal to wall-sized. Our approach was open-ended rather than grounded in existing interaction zone models in order to explore potential differences in interaction zones and distances. We found that the existence of different interaction zones and the distance at which these zones are relevant are dependent on display size. In discussion of the results, we explore implications of our findings and offer guidelines for the design of interactive display systems.

Event-based text visual analytics

We present an event-based approach for solving a directed sensemaking task in which we combine powerful information foraging tools with intuitive synthesis spaces to solve the VAST Challenge 2014 Mini-Challenge 1. A combination of student-created and commericially available software are used to solve various aspects of the scenario. In addition to applying entitiy extraction and topic modelling, we enable the user to explore a large dataset using multi-model semantic interaction, which infers analytical reasoning from user actions to augment the data spatialization and determine what information should be presented and suggested to the user. Additionally, we visualize extracted topics using Tableau to construct a timeline of events surrounding the questions posed by the challenge.