Background and Feasibility

Surveillance of marine areas, in particular in determining the exact location and activity of vessels, is a key challenge for navigation. It has a direct influence on the Maritime Economy and the successful implementation of relevant national and European regulatory frameworks. In the surveillance of marine areas, the use of navigation data, which feeds decision-making systems at various levels (business, environmental, administrative). Examples of such data are: (a) VMS1 data (Vessel Monitoring Systems) emitted by the specific equipment on board the fishing vessels; (b) AIS2 (Automatic Identification System) data transmitted by all vessels having an obligation under international navigation rules; (c) data transmitted by electronic means of maritime surveillance (radars, thermal cameras)..

However, a number of research and technology issues in the management of these data create significant barriers to their use by decision-making systems. Specifically:

  • Accuracy of data. According to relevant studies3, vessels often transmit incomplete identification and activity data, sometimes malicious and sometimes due to omission.
  • Data volume. Creating an effective "Business Image" (real and historical time) for monitoring marine areas requires the combined use of location data with spatial, meteorological and environmental data.

A significant part of these data is now available as Open Data (eg, Their use can, not only improve the accuracy of identification functions, but also provide useful qualitative information to extract patterns of navigation behavior.

The need for combined use of data leads to large volume and complexity databases.

  • Real-time analysis. The analysis of navigation data requires the merging of data streams, ie data that (a) continuously change (data speed), and (b) come from multiple sources (data variety) in real time.

Exactly the four-dimensional (Veracity, Volume, Velocity, Variety), which is internationally known as Veracity, Volume, Velocity, Variety, is what characterizes marine data surveillance as Big Data.

Exploiting large data on shipping has direct implications both for the overall competitiveness of sea-related economic activities and for sustainability.