The goal of visual analytical tools is to support the analytical reasoning process, maximizing human perceptual,
understanding and reasoning capabilities in complex and dynamic situations. Visual analytics software must be
built upon an understanding of the reasoning process, since it must provide appropriate interactions that allow a
true discourse with the information. In order to deepen our understanding of the human analytical process and
guide developers in the creation of more efficient anomaly detection systems, this paper investigates how is the
human analytical process of detecting and identifying anomalous behavior in maritime traffic data. The main
focus of this work is to capture the entire analysis process that an analyst goes through, from the raw data to
the detection and identification of anomalous behavior.
Three different sources are used in this study: a literature survey of the science of analytical reasoning,
requirements specified by experts from organizations with interest in port security and user field studies conducted
in different marine surveillance control centers. Furthermore, this study elaborates on how to support the human
analytical process using data mining, visualization and interaction methods.
The contribution of this paper is twofold: (1) within visual analytics, contribute to the science of analytical
reasoning with practical understanding of users tasks in order to develop a taxonomy of interactions that support
the analytical reasoning process and (2) within anomaly detection, facilitate the design of future anomaly detector
systems when fully automatic approaches are not viable and human participation is needed.