Monitoring the surveillance of large sea areas normally involves the analysis of huge quantities of heterogeneous
data from multiple sources (radars, cameras, automatic identification systems, reports, etc.). The rapid
identification of anomalous behavior or any threat activity in the data is an important objective for enabling
homeland security. While it is worth acknowledging that many existing mining applications support identification
of anomalous behavior, autonomous anomaly detection systems are rarely used in the real world. There
are two main reasons: (1) the detection of anomalous behavior is normally not a well-defined and structured
problem and therefore, automatic data mining approaches do not work well and (2) the difficulties that these
systems have regarding the representation and employment of the prior knowledge that the users bring to their
tasks. In order to overcome these limitations, we believe that human involvement in the entire discovery process
Using a visual analytics process model as a framework, we present VISAD: an interactive, visual knowledge
discovery tool for supporting the detection and identification of anomalous behavior in maritime traffic data.
VISAD supports the insertion of human expert knowledge in (1) the preparation of the system, (2) the establishment
of the normal picture and (3) in the actual detection of rare events. For each of these three modules,
VISAD implements different layers of data mining, visualization and interaction techniques. Thus, the detection
procedure becomes transparent to the user, which increases his/her confidence and trust in the system and
overall, in the whole discovery process.