標(biāo)題: Titlebook: Big Data Analytics for Time-Critical Mobility Forecasting; From Raw Data to Tra George A. Vouros,Gennady Andrienko,David Scarlatti Book 202 [打印本頁] 作者: 閘門 時(shí)間: 2025-3-21 19:26
書目名稱Big Data Analytics for Time-Critical Mobility Forecasting影響因子(影響力)
書目名稱Big Data Analytics for Time-Critical Mobility Forecasting影響因子(影響力)學(xué)科排名
書目名稱Big Data Analytics for Time-Critical Mobility Forecasting網(wǎng)絡(luò)公開度
書目名稱Big Data Analytics for Time-Critical Mobility Forecasting網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Big Data Analytics for Time-Critical Mobility Forecasting被引頻次
書目名稱Big Data Analytics for Time-Critical Mobility Forecasting被引頻次學(xué)科排名
書目名稱Big Data Analytics for Time-Critical Mobility Forecasting年度引用
書目名稱Big Data Analytics for Time-Critical Mobility Forecasting年度引用學(xué)科排名
書目名稱Big Data Analytics for Time-Critical Mobility Forecasting讀者反饋
書目名稱Big Data Analytics for Time-Critical Mobility Forecasting讀者反饋學(xué)科排名
作者: calorie 時(shí)間: 2025-3-21 22:40 作者: Albinism 時(shí)間: 2025-3-22 03:00
Visual Analytics in the Aviation and Maritime Domains in data analysis and problem solving. It develops a methodology of analysis that facilitates human activities by means of interactive visual representations of information. By examples from the domains of aviation and maritime transportation, we demonstrate the essence of the visual analytics metho作者: 音樂學(xué)者 時(shí)間: 2025-3-22 04:45
Trajectory Detection and Summarization over Surveillance Data Streamsifically for sailing vessels and flying aircraft. Assuming that surveillance data monitoring their locations over a large geographical area is available in a streaming fashion, this novel methodology drops any predictable positions (along trajectory segments of “normal” motion characteristics) with 作者: 符合國情 時(shí)間: 2025-3-22 12:39 作者: 詞根詞綴法 時(shí)間: 2025-3-22 16:40 作者: definition 時(shí)間: 2025-3-22 18:12 作者: insomnia 時(shí)間: 2025-3-22 22:40 作者: MORT 時(shí)間: 2025-3-23 02:41
Event Processing for Maritime Situational Awarenessnd garbage, smuggling, piracy and more. We present our efforts to combine two stream reasoning technologies for detecting such activities in real time: a formal, computational framework for composite maritime event recognition, based on the Event Calculus, and an industry-strong maritime anomaly det作者: FLAG 時(shí)間: 2025-3-23 06:33
Offline Trajectory Analyticsphones and tablets. This massive-scale data generation has posed new challenges in the data management community in terms of storing, querying, analyzing, and extracting knowledge out of such data. Knowledge discovery out of mobility data is essentially the goal of every mobility data analytics task作者: anticipate 時(shí)間: 2025-3-23 11:18
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作者: ambivalence 時(shí)間: 2025-3-23 15:45 作者: 挫敗 時(shí)間: 2025-3-23 20:41
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.作者: 脖子 時(shí)間: 2025-3-23 23:54 作者: 柱廊 時(shí)間: 2025-3-24 05:42
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.作者: 圍裙 時(shí)間: 2025-3-24 08:46 作者: Cpap155 時(shí)間: 2025-3-24 12:43
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.作者: 說笑 時(shí)間: 2025-3-24 15:10 作者: carotid-bruit 時(shí)間: 2025-3-24 21:32 作者: vertebrate 時(shí)間: 2025-3-24 23:15
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.作者: Blatant 時(shí)間: 2025-3-25 06:06 作者: 頌揚(yáng)國家 時(shí)間: 2025-3-25 10:08
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.作者: Ischemic-Stroke 時(shí)間: 2025-3-25 14:47 作者: Culmination 時(shí)間: 2025-3-25 15:49
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.作者: JECT 時(shí)間: 2025-3-25 20:01 作者: GRILL 時(shí)間: 2025-3-26 00:08
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.作者: TAP 時(shí)間: 2025-3-26 05:58
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.作者: tackle 時(shí)間: 2025-3-26 08:27 作者: Brain-Imaging 時(shí)間: 2025-3-26 14:16 作者: ornithology 時(shí)間: 2025-3-26 20:33
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.作者: Bother 時(shí)間: 2025-3-27 00:32
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.作者: 狗舍 時(shí)間: 2025-3-27 04:54
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.作者: 慎重 時(shí)間: 2025-3-27 05:20
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作者: follicular-unit 時(shí)間: 2025-3-27 09:28 作者: 仔細(xì)檢查 時(shí)間: 2025-3-27 13:48 作者: floodgate 時(shí)間: 2025-3-27 20:10 作者: 無孔 時(shí)間: 2025-3-27 23:33 作者: 環(huán)形 時(shí)間: 2025-3-28 05:17
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作者: 合乎習(xí)俗 時(shí)間: 2025-3-28 09:09 作者: conscience 時(shí)間: 2025-3-28 10:54
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作者: 沒花的是打擾 時(shí)間: 2025-3-28 16:47 作者: municipality 時(shí)間: 2025-3-28 19:37 作者: 小溪 時(shí)間: 2025-3-28 23:40 作者: Dappled 時(shí)間: 2025-3-29 05:38 作者: 古董 時(shí)間: 2025-3-29 08:33 作者: curettage 時(shí)間: 2025-3-29 11:44
http://image.papertrans.cn/b/image/185617.jpg作者: 合同 時(shí)間: 2025-3-29 15:57 作者: 清醒 時(shí)間: 2025-3-29 21:58 作者: 6Applepolish 時(shí)間: 2025-3-30 02:26
