KEYWORDS: Visualization, Data modeling, Data mining, Visual analytics, Visual process modeling, Analytical research, Data processing, Sensors, Surveillance, Network security
The surveillance of large sea, air or land areas normally involves the analysis of large volumes of heterogeneous
data from multiple sources. Timely detection and identification of anomalous behavior or any threat activity
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 for
area surveillance are rarely used in the real world. We argue that such capabilities and applications present two
critical challenges: (1) they need to provide adequate user support and (2) they need to involve the user in the
underlying detection process.
In order to encourage the use of anomaly detection capabilities in surveillance systems, this paper analyzes
the challenges that existing anomaly detection and behavioral analysis approaches present regarding their use
and maintenance by users. We analyze input parameters, detection process, model representation and outcomes.
We discuss the role of visualization and interaction in the anomaly detection process. Practical examples from
our current research within the maritime domain illustrate key aspects presented.
KEYWORDS: Visualization, Visual analytics, Data modeling, Data mining, Sensors, Visual process modeling, Artificial intelligence, Process modeling, Systems modeling, Human-machine interfaces
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
is crucial.
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.
KEYWORDS: Visual analytics, Visualization, Data mining, Data processing, Analytical research, Data modeling, Information visualization, Radar, Computer security, Homeland security
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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.