Browsing Reprints by Subject "Big data"
Now showing items 1-7 of 7
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Automated port traffic statistics: from raw data to visualization
(CMRE, 2017/11)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 ... -
Context for maritime situation awareness
(CMRE, 2019/06)Effective maritime situation awareness (MSA) relies on high-level information fusion tasks that benefit from a formal approach to context-based reasoning. This chapter discusses the relative notion of context through the ... -
A distributed approach to estimating sea port operational regions from lots of AIS data
(CMRE, 2019/06)Seaports play a vital role in the global economy, as they operate as the connection corridors to all other modes of transport and as engines of growth for the wider region. But ports today are faced with numerous unique ... -
A document-based data model for large scale computational maritime situational awareness
(CMRE, 2019/06)Computational Maritime Situational Awareness (MSA) supports the maritime industry, governments, and international organizations with machine learning and big data techniques for analyzing vessel traffic data available ... -
Mining maritime vessel traffic: promises, challenges, techniques
(CMRE, 2019/06)This paper discusses machine learning and data mining approaches to analyzing maritime vessel traffic based on the Automated Information System (AIS). We review recent efforts to apply machine learning techniques to AIS ... -
Scalable and distributed sea port operational areas estimation from AIS data
(CMRE, 2019/06)Seaports are spatial units that do not remain static over time. They are constantly in flux, evolving according to environmental and connectivity patterns both in size and operational capacity. As such any valid decision ... -
Scalable distributed change detection and its application to maritime traffic
(CMRE, 2019/06)Building on a novel methodology based on the Ornstein-Uhlenbeck (OU) process to perform accurate long-term predictions of future positions of ships at sea, we present a statistical approach to the detection of abrupt changes ...
