A Document-based Data Model for Large Scale Computational Maritime Situational Awareness

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Luca Cazzanti, Leonardo M. Millefiori, Gianfranco Arcieri
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Abstract - 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 through the Automatic Identification System (AIS). A critical challenge of scaling computational MSA to big data regimes is integrating the core learning algorithms with big data storage modes and data models. To address this challenge, we report results from our experimentation with MongoDB, a NoSQL document-based database which we test as a supporting platform for computational MSA. We experiment with a document model that avoids database joins when linking position and voyage AIS vessel information and allows tuning the database index and document sizes in response to the AIS data rate. We report results for the AIS data ingested and analyzed daily at the NATO Centre for Maritime Research and Experimentation (CMRE).

BibTex -
@INPROCEEDINGS{@InProceedings{CazzantiIEEEBigData2015, TITLE = {A Document-based Data Model for Large Scale Computational Maritime Situational Awareness}, AUTHOR = {Cazzanti, Luca and Millefiori, Leonardo M. and Arcieri, Gianfranco}, BOOKTITLE = {Big Data, 2015. Proceedings of IEEE}, YEAR = {2015}, MONTH = {October}, ORGANIZATION = {IEEE} }