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Big data analytics for time critical maritime and aerial mobility forecasting

Vouros, G., Doulkeridis, C., Santipantakis, G. , Vlachou, A., Pelekis, N., Georgiou, H., Theodoridis, Y., Patroumpas, K., Alevizos, E., Artikis, A., Fuchs, G., Mock, M., Andrienko, G. ORCID: 0000-0002-8574-6295, Andrienko, N. ORCID: 0000-0003-3313-1560, Ray, C., Claramunt, C., Camossi, E., Jousselme, A-L., Scarlatti, D. & Cordero, J. M. (2018). Big data analytics for time critical maritime and aerial mobility forecasting. Advances in Database Technology - EDBT, 2018, pp. 612-623. doi: 10.5441/002/edbt.2018.71


The correlated exploitation of heterogeneous data sources offering very large archival and streaming data is important to increase the accuracy of computations when analysing and predicting future states of moving entities. Aiming to significantly advance the capacities of systems to improve safety and effectiveness of critical operations involving a large number of moving entities in large geographical areas, this paper describes progress achieved towards time critical big data analytics solutions to user-defined challenges in the air-traffic management and maritime domains. Besides, this paper presents further research challenges concerning data integration and management, predictive analytics for trajectory and events forecasting, and visual analytics.

Publication Type: Article
Additional Information: © 2018 Copyright held by the owner/author(s)
Subjects: G Geography. Anthropology. Recreation > GA Mathematical geography. Cartography
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Science & Technology > Computer Science
School of Science & Technology > Computer Science > giCentre
Text - Published Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

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