Titlebook: Big Data Analytics for Time-Critical Mobility Forecasting; From Raw Data to Tra George A. Vouros,Gennady Andrienko,David Scarlatti Book 202

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The , Big Data Architecture for Mobility Analyticssources, this chapter presents the . architecture: Denoting “difference,” . emphasizes on the different processing requirements from loosely coupled components, which serve intertwined processing purposes, forming processing pipelines. The . architecture, being a generic architectural paradigm for r

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https://doi.org/10.1007/978-1-4842-5380-9owledge. We describe four case studies in which distinct kinds of knowledge have been derived from trajectories of vessels and airplanes and related spatial and temporal data by human analytical reasoning empowered by interactive visual interfaces combined with computational operations.

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https://doi.org/10.1007/978-1-349-22595-8 as from individual components and pipelines. The chapter presents the datAcron integrated system as a specific instantiation of the . architecture, aiming to satisfy requirements for big data mobility analytics, exploiting real-world mobility data for performing real-time and batch analysis tasks.

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Modeling Mobility Data and Constructing Large Knowledge Graphs to Support Analytics: The datAcron Onrajectories, at multiple, interlinked levels of detail. In addition, we show that this ontology supports data transformations that are required for performing advanced analytics tasks, such as visual analytics, and we present use-case scenarios in the Air Traffic Management and maritime domains.

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https://doi.org/10.1007/978-1-349-25536-8us sources for maritime surveillance is finally described, gathering 13 sources. This chapter concludes on the generation of specific datasets to be used for algorithms evaluation and comparison purposes.