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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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21#
發(fā)表于 2025-3-25 06:06:19 | 只看該作者
22#
發(fā)表于 2025-3-25 10:08:32 | 只看該作者
Women, Violence and Male Power,nges regarding mobility patterns in terms of points or trajectories, respectively. It is expected that these modeling approaches can be transferred to other domains of similar challenges and with similar success.
23#
發(fā)表于 2025-3-25 14:47:31 | 只看該作者
24#
發(fā)表于 2025-3-25 15:49:40 | 只看該作者
Mobility Data: A Perspective from the Maritime Domainus 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.
25#
發(fā)表于 2025-3-25 20:01:54 | 只看該作者
26#
發(fā)表于 2025-3-26 00:08:22 | 只看該作者
Future Location and Trajectory Predictionnges regarding mobility patterns in terms of points or trajectories, respectively. It is expected that these modeling approaches can be transferred to other domains of similar challenges and with similar success.
27#
發(fā)表于 2025-3-26 05:58:40 | 只看該作者
Offline Trajectory Analyticsh scenarios, an analyst should be able to apply, at massive scale, several knowledge discovery techniques, such as trajectory clustering, hotspot analysis, and frequent route/network discovery methods.
28#
發(fā)表于 2025-3-26 08:27:37 | 只看該作者
29#
發(fā)表于 2025-3-26 14:16:46 | 只看該作者
30#
發(fā)表于 2025-3-26 20:33:39 | 只看該作者
https://doi.org/10.1007/978-1-349-25726-3: 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.
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