To complement the currently existing definitions and conceptual frameworks of visual analytics, which focus mainly on activities performed by analysts and types of techniques they use, we attempt to define the expected results of these activities. We argue that the main goal of doing visual analytics is to build a mental and/or formal model of a certain piece of reality reflected in data. The purpose of the model may be to understand, to forecast or to control this piece of reality. Based on this model‐building perspective, we propose a detailed conceptual framework in which the visual analytics process is considered as a goal‐oriented workflow producing a model as a result. We demonstrate how this framework can be used for performing an analytical survey of the visual analytics research field and identifying the directions and areas where further research is needed.
User tasks play a pivotal role in visualization design and evaluation. However, the term ‘task’ is used ambiguously within the visualization community. In this article, we critically analyze the relevant literature and systematically compare definitions of ‘task’ and the usage of related terminology. In doing so, we identify a three-dimensional conceptual space of user tasks in visualization, referred to as the task cube, and the more precise concepts ‘objective’ and ‘action’ for tasks. We illustrate the usage of the task cube’s dimensions in an objective-driven visualization process, in different scenarios of visualization design and evaluation, and for comparing categorizations of abstract tasks. Thus, visualization researchers can better formulate their contributions which helps advance visualization as a whole.
Behavior-based analysis of emerging malware families involves finding suspicious patterns in large collections of execution traces. This activity cannot be automated for previously unknown malware families and thus malware analysts would benefit greatly from integrating visual analytics methods in their process. However existing approaches are limited to fairly static representations of data and there is no systematic characterization and abstraction of this problem domain. Therefore we performed a systematic literature study, conducted a focus group as well as semi-structured interviews with 10 malware analysts to elicit a problem abstraction along the lines of data, users, and tasks. The requirements emerging from this work can serve as basis for future design proposals to visual analytics-supported malware pattern analysis.
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