anticipate
发表于 2025-3-23 11:18:53
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
ambivalence
发表于 2025-3-23 15:45:17
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挫败
发表于 2025-3-23 20:41:26
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.
脖子
发表于 2025-3-23 23:54:51
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柱廊
发表于 2025-3-24 05:42:35
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.
围裙
发表于 2025-3-24 08:46:37
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Cpap155
发表于 2025-3-24 12:43:59
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.
说笑
发表于 2025-3-24 15:10:30
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carotid-bruit
发表于 2025-3-24 21:32:29
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vertebrate
发表于 2025-3-24 23:15:03
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.