Black Holes, Keyholes And Brown Worms: Challenges In Sense Making

Abstract

We address the problems faced by analysts who have to sift through large amounts of data quickly and accurately in order to make sense of the information contained within the data or the circumstance represented in the data. We discuss the approach we have taken to visual analytics from the perspective of the Data-Frame Theory of Sense-making and its extension to Causal Reasoning, and how the cognitive strategies that are invoked in these processes need to be supported. We identify 20 problems that designers of visual analytics-type systems need to address in order to support sense-making. In particular, we discuss design issues associated with three exemplar problems: (i) Black holes -the problem of representing missing data; (ii) Keyholes -the problem of being able to access and view only a small part of a large dataset or only part of a problem; and (iii) Brown worms -the problem of dealing with and representing misleading or deceptive data.

How Analysts Think (?): Early Observations

“…Ideally, analysts should proceed beyond the descriptive and explanatory levels to synthesis and effective persuasion, to produce action-able intelligence. In Figure 1, we illustrate only a number of the problems encountered by intelligence analysts [6]: (i) the jig-saw puzzle problem. Analysts are presented with information that may be akin to a jig-saw puzzle.…”

“…The studies include a set of focus group studies with 20 intelligence analysts [6], think-aloud studies with 6 analysts performing a simulated intelligence task [7], thinkaloud studies with 6 librarians carrying out a surrogate task of creating explanations from a literature review task [8].…”

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

“…Another type of uncertainty stems from the un-awareness that there are missing data. One method to address the missing data problem was proposed in Wong and Varga (2012), called 'black holes', or gaps in a sequence of data. These gaps or black holes create opportunities for the analyst to ask "why?…”

Interactive Visualization for Information Analysis in Medical Diagnosis

“…These areas, though described as problems, represent areas where Information Analysts can benefit from more advanced science and technology [19]. The problems have been gathered and condensed from across several studies, and a series of interviews and focus group discussions with researchers and students interacting with library electronic resource systems, legal investigators, and information analysts.…”

“…This similarity allows us to consider our work in the intelligence, legal investigation and e-discovery domains, in the context of medical diagnostic analysis, particularly in the area of reviewing a patient's medical history for the purpose of developing treatment plans. From our work in designing interactive visualizations for information analysis, we documented a list of 20 design problems [19], and will discuss five problems that we believe have a bearing in designing medical diagnostic displays that can assist in improving the review of a patient's medical history. We will then discuss the design issues and a number of possible designs currently under consideration in the context of a visual sense making system INVISQUE [20].…”