Maritime data integration and analysis: recent progress and research challenges

The correlated exploitation of heterogeneous data sources offering very large historical as well as streaming data is important to increasing the accuracy of computations when analysing and predicting future states of moving entities. This is particularly critical in the maritime domain, where online tracking, early recognition of events, and real-time forecast of anticipated trajectories of vessels are crucial to safety and operations at sea. The objective of this paper is to review current research challenges and trends tied to the integration, management, analysis, and visualization of objects moving at sea as well as a few suggestions for a successful development of maritime forecasting and decision-support systems.

[1]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[2]  Miss A.O. Penney.  (b) , 1974, The New Yale Book of Quotations.

[3]  Andreas Stephanik,et al.  A Database-Supported Workbench for Information Fusion: INFUSE , 2002, EDBT.

[4]  장윤희,et al.  Y. , 2003, Industrial and Labor Relations Terms.

[5]  Peter Willett,et al.  Radar/AIS data fusion and SAR tasking for Maritime Surveillance , 2008, 2008 11th International Conference on Information Fusion.

[6]  Gerben de Vries,et al.  Combining ship trajectories and semantics with the simple event model (SEM) , 2009, EiMM '09.

[7]  Yassine Lassoued,et al.  Ontologies and Ontology Extension for Marine Environmental Information Systems , 2010 .

[8]  Paulo Cesar G. da Costa,et al.  Modeling a probabilistic ontology for Maritime Domain Awareness , 2011, 14th International Conference on Information Fusion.

[9]  Bhavani M. Thuraisingham,et al.  Heuristics-Based Query Processing for Large RDF Graphs Using Cloud Computing , 2011, IEEE Transactions on Knowledge and Data Engineering.

[10]  Ioannis Konstantinou,et al.  H2RDF: adaptive query processing on RDF data in the cloud. , 2012, WWW.

[11]  Aldo Napoli,et al.  An enhanced spatial reasoning ontology for maritime anomaly detection , 2012, 2012 7th International Conference on System of Systems Engineering (SoSE).

[12]  It's not about the data , 2012, Nature Genetics.

[13]  Axel-Cyrille Ngonga Ngomo,et al.  On Link Discovery using a Hybrid Approach , 2012, Journal on Data Semantics.

[14]  Shashi Shekhar,et al.  Spatial big-data challenges intersecting mobility and cloud computing , 2012, MobiDE '12.

[15]  Manolis Koubarakis,et al.  Strabon: A Semantic Geospatial DBMS , 2012, SEMWEB.

[16]  Alessandro Margara,et al.  Processing flows of information: From data stream to complex event processing , 2012, CSUR.

[17]  Ralf Hartmut Güting,et al.  Parallel SECONDO: Practical and efficient mobility data processing in the cloud , 2013, 2013 IEEE International Conference on Big Data.

[18]  James Llinas,et al.  Challenges in Information Fusion Technology Capabilities for Modern Intelligence and Security Problems , 2013, 2013 European Intelligence and Security Informatics Conference.

[19]  Wan Fokkink,et al.  Assessing Trust for Determining the Reliability of Information , 2013, Situation Awareness with Systems of Systems.

[20]  Haixun Wang,et al.  A Distributed Graph Engine for Web Scale RDF Data , 2013, Proc. VLDB Endow..

[21]  Min Wang,et al.  EAGRE: Towards scalable I/O efficient SPARQL query evaluation on the cloud , 2013, 2013 IEEE 29th International Conference on Data Engineering (ICDE).

[22]  Stefano Spaccapietra,et al.  Semantic trajectories modeling and analysis , 2013, CSUR.

[23]  Marco Balduzzi,et al.  A security evaluation of AIS automated identification system , 2014, ACSAC.

[24]  Helbert Arenas,et al.  A Semantic analysis of moving objects, using as a case study maritime voyages from eighteenth and nineteenth centuries , 2014 .

[25]  Martin Theobald,et al.  TriAD: a distributed shared-nothing RDF engine based on asynchronous message passing , 2014, SIGMOD Conference.

[26]  Nikos Mamoulis,et al.  An Effective Encoding Scheme for Spatial RDF Data , 2014, Proc. VLDB Endow..

[27]  Hussein A. Abbass,et al.  Risk management with hard-soft data fusion in maritime domain awareness , 2014, the 2014 Seventh IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA).

[28]  Talel Abdessalem,et al.  Integration of Web Sources Under Uncertainty and Dependencies Using Probabilistic XML , 2014, DASFAA Workshops.

[29]  Wojciech Jamrozik,et al.  Concept of Database Architecture Dedicated to Data Fusion Based Condition Monitoring Systems , 2014, BDAS.

[30]  Nikos Pelekis,et al.  Event Recognition for Maritime Surveillance , 2015, EDBT.

[31]  Ahmed Eldawy,et al.  The era of big spatial data , 2015, 2015 31st IEEE International Conference on Data Engineering Workshops.

[32]  Xiaohui Yu,et al.  Scalable Distributed Processing of K Nearest Neighbor Queries over Moving Objects , 2015, IEEE Transactions on Knowledge and Data Engineering.

[33]  Jesús García,et al.  Context-based Information Fusion: A survey and discussion , 2015, Inf. Fusion.

[34]  Michael Mock,et al.  Towards Flexible Event Processing in Distributed Data Streams , 2015, EDBT/ICDT Workshops.

[35]  Alan M. MacEachren.  Visual Analytics and Uncertainty: Its Not About the Data , 2015, EuroVA@EuroVis.

[36]  Alain Bouju,et al.  DeAIS project: Detection of AIS spoofing and resulting risks , 2015, OCEANS 2015 - Genova.

[37]  Guohui Xiao,et al.  Ontology-Based Data Access for Maritime Security , 2016, ESWC.

[38]  Weiru Liu,et al.  The basic principles of uncertain information fusion. An organised review of merging rules in different representation frameworks , 2016, Inf. Fusion.

[39]  Christopher N. Eichelberger,et al.  A survey of techniques and open-source tools for processing streams of spatio-temporal events , 2016, IWGS@SIGSPATIAL.

[40]  Cyril Ray,et al.  Design principles of a stream-based framework for mobility analysis , 2016, GeoInformatica.

[41]  Juliana Freire,et al.  Data Polygamy: The Many-Many Relationships among Urban Spatio-Temporal Data Sets , 2016, SIGMOD Conference.

[42]  Anne-Laure Jousselme,et al.  Uncertainty Representations for Information Retrieval with Missing Data , 2016 .

[43]  Ahmed Eldawy,et al.  The era of Big Spatial Data , 2016, ICDE.

[44]  Hoan Quoc Nguyen-Mau,et al.  The Graph of Things: A step towards the Live Knowledge Graph of connected things , 2016, J. Web Semant..

[45]  Gennady L. Andrienko,et al.  Understanding movement data quality , 2016, J. Locat. Based Serv..