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

Bother

The Perspective on Mobility Data from the Aviation Domainves. In order to do this, new concepts of operations are arising, such as trajectory-based operations, which open many new possibilities in terms of system predictability, paving the way for the application of big data techniques in the Aviation Domain. This chapter presents the state of the art in these matters.

狗舍

Event Processing for Maritime Situational Awareness: a formal, computational framework for composite maritime event recognition, based on the Event Calculus, and an industry-strong maritime anomaly detection service, capable of processing daily real-world data volumes.

慎重

https://doi.org/10.1007/978-1-349-25536-8 with the detection of threats and abnormal activities. The maritime use cases and scenarios are geared on fishing activities monitoring, aligning with the European Union Maritime Security Strategy. Six scenarios falling under three use cases are presented together with maritime situational indicato

follicular-unit

仔细检查

floodgate

无孔

环形

Understanding C# and the .NET Frameworkseveral tasks, such as data deduplication, record linkage, and data integration. Existing LD frameworks facilitate data integration tasks over multidimensional data. However, limited work has focused on spatial or spatiotemporal LD, which is typically much more processing-intensive due to the comple

合乎习俗

conscience

Women, Violence and Male Power,t pillar is the problem formulation regarding two complementary tasks, namely the . (FLP) and the . (TP). The second pillar tackles the issue of effectiveness, efficiency, and scalabilityfor the corresponding predictive analytics models for big fleets of moving objects. Finally, the third pillar tak