| dc.contributor.author | Cazzanti, Luca | |
| dc.contributor.author | Davoli, Antonio | |
| dc.contributor.author | Millefiori, Leonardo | |
| dc.date.accessioned | 2018-10-11T14:09:56Z | |
| dc.date.available | 2018-10-11T14:09:56Z | |
| dc.date.issued | 2017/11 | |
| dc.identifier | 101475 | |
| dc.identifier.govdoc | CMRE-PR-2017-010 | |
| dc.identifier.uri | http://hdl.handle.net/20.500.12489/706 | |
| dc.description.abstract | We describe how we leveraged best practices in big data processing pipeline design and visual analytics to prototype the Maritime Patterns-of-Life Information Service (MPoLIS), an information product currently under devel-opment at the NATO Centre for Maritime Research and Experimentation (CMRE). MPoLIS supports the maritime industry, governments, and international organizations with visual analytics on vessel traffic in seaports. It addresses three main requirements: a) storing and processing large amounts of data; b) on-demand availability of statistical summaries of vessel traffic in ports; c) intuitive and interactive interface for subject matter experts (SMEs) in the maritime domain. MPoLIS has contributed to building a data-driven, self-service analytics culture within NATO and has been sanctioned for use in support of maritime situational awareness (MSA) in ongoing NATO operations. | |
| dc.format | 5 p. : ill. ; digital, PDF file | |
| dc.language | English | |
| dc.publisher | CMRE | |
| dc.source | In: 2016 IEEE International Conference on Big Data (Big Data) | |
| dc.subject | Maritime situational awareness | |
| dc.subject | Big data | |
| dc.subject | Ship movements | |
| dc.subject | Ship tracking | |
| dc.title | Automated port traffic statistics: from raw data to visualization | |
| dc.type | Reprint (PR) | |
| dc.type | Papers and Articles | |
