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.

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