Using Interactive Visual Reasoning to Support Sense-Making: Implications for Design

Abstract

This research aims to develop design guidelines for systems that support investigators and analysts in the exploration and assembly of evidence and inferences. We focus here on the problem of identifying candidate 'influencers' within a community of practice. To better understand this problem and its related cognitive and interaction needs, we conducted a user study using a system called INVISQUE (INteractive Visual Search and QUery Environment) loaded with content from the ACM Digital Library. INVISQUE supports search and manipulation of results over a freeform infinite 'canvas'. The study focuses on the representations user create and their reasoning process. It also draws on some pre-established theories and frameworks related to sense-making and cognitive work in general, which we apply as a 'theoretical lenses' to consider findings and articulate solutions. Analysing the user-study data in the light of these provides some understanding of how the high-level problem of identifying key players within a domain can translate into lower-level questions and interactions. This, in turn, has informed our understanding of representation and functionality needs at a level of description which abstracts away from the specifics of the problem at hand to the class of problems of interest. We consider the study outcomes from the perspective of implications for design.

The State of the Art in Integrating Machine Learning into Visual Analytics

“…To create such a design, there is a need to have a conception of the analytical thinking and reasoning process that extends beyond the information handling and manipulation aspects that are frequently described. A focus group study with 20 intelligence analysts [WV12], think‐aloud studies with 6 analysts performing a simulated intelligence task [RAWC14], and think‐aloud studies with 6 librarians carrying out a surrogate task of creating explanations from a literature review task [KAW*13] provide insight into this analytical thinking and reasoning process. The results of these studies indicate that analyst make use of the various inference making strategies described in Section – induction, deduction and adduction—depending upon what data they have, the rules for interpreting the data, and premise they are starting with and the conclusions they would make or would like to make.…”

Supporting Variability in Criminal Intelligence Analysis: From Expert Intuition to Critical and Rigorous Analysis

“…Based on a number of studies we conducted: e.g. focus group studies with 20 intelligence analysts (Wong and Varga 2012); think-aloud studies with analysts and librarians performing simulated intelligence tasks (Rooney et al 2014;Kodagoda et al 2013); and cognitive task analyses with analysts from three major police forces in Europe (e.g. Wong and Kodagoda 2016;Gerber et al 2016), we summarise below the key design requirements for fluidity in analytical reasoning.…”

“…In addition, VALCRI design encourages the use of imagination to generate new possibilities, ideas or concepts beyond what is presented. Kodagoda et al (2013) discovered that during the early stages of analysis, once the participants discovered patterns or semantically meaningful connections in the data set, they were able to identify anchors from which to spawn new searches.…”

Developing a Model of Distributed Sensemaking: A Case Study of Military Analysis

“…Visualisation tools are defined as computer-based tools that visually represent data/information in ways that allow interaction with them, allowing users to interpret and analyse data [19]. Work in visual analytics [20], information visualisation and scientific visualisation, among others, has led to a number of developments in computer-based visualisation tools, including a number of tools that support sensemaking [21]. Two such examples include INVISQUE and JIGSAW.…”