Regarding the application to the maritime domain, the algorithms being developed would thus support the detection of abnormal activity in large vessel fleets across large geographical areas, as well as the assessment of any threat for an early intervention. In other words, datAcron would support the decision maker in reaching a level of situation awareness for an informed decision.

This challenging and crucial task is at the core of the compilation of a maritime picture which involves extracting relevant contextual information but also monitoring the real time maritime traffic. The use of a set of sensors mixing cooperative self-identication systems such as the Automatic Identication System (AIS) and non-cooperative systems such as coastal radars or satellite imagery provides the necessary complementarity and redundancy of information to overcome the possible and quite common spoong of AIS signals while increasing the clarity and accuracy of the maritime picture. In many cases, intelligence information can also be helpful in rening and guiding the search in the huge amount of data to be processed,filtered and analysed, as well as representing the contextual information for some MSA problems.

Facing the huge volume of various information with high velocity which often lacks veracity , a system to automatically process both historical and timely information would greatly support the operator in monitoring and analysis tasks. The maritime use case described in this document emphasises the use of datAcron in a human-centric automatic processing of data, stressing the role of the user (or decision maker) in his interaction with the datAcron system.