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Big data analytics for time-critical...

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Big data analytics for time-critical mobility forecastingfrom raw data to trajectory-oriented mobility analytics in the aviation and maritime domains /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Big data analytics for time-critical mobility forecastingedited by George A. Vouros ... [et al.].
其他題名:	from raw data to trajectory-oriented mobility analytics in the aviation and maritime domains /
其他作者:	Vouros, George A.
出版者:	Cham :Springer International Publishing :2020.
面頁冊數:	xxxii, 361 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	TransportationData processing.
電子資源:	https://doi.org/10.1007/978-3-030-45164-6
ISBN:	9783030451646$q(electronic bk.)

Big data analytics for time-critical mobility forecastingfrom raw data to trajectory-oriented mobility analytics in the aviation and maritime domains /
Big data analytics for time-critical mobility forecastingfrom raw data to trajectory-oriented mobility analytics in the aviation and maritime domains /[electronic resource] :edited by George A. Vouros ... [et al.]. - Cham :Springer International Publishing :2020. - xxxii, 361 p. :ill., digital ;24 cm.

Part I: Time Critical Mobility Operations and Data: A Perspective from the Maritime and Aviation Domains -- Mobility Data: A Perspective from the Maritime Domain -- The Perspective on Mobility Data from the Aviation Domain -- Part II: Visual Analytics and Trajectory Detection and Summarization: Exploring Data and Constructing Trajectories -- Visual Analytics in the Aviation and Maritime Domains -- Trajectory Detection and Summarization over Surveillance Data Streams -- Part III: Trajectory Oriented Data Management for Mobility Analytics -- Modeling Mobility Data and Constructing Large Knowledge Graphs to Support Analytics: The datAcron Ontology -- Integrating Data by Discovering Topological and Proximity Relations Among Spatiotemporal Entities -- Distributed Storage of Large Knowledge Graphs with Mobility Data -- Part IV: Analytics Towards Time Critical Mobility Forecasting -- Future Location and Trajectory Prediction -- Event Processing for Maritime Situational Awareness -- Offline Trajectory Analytics -- Part V Big Data Architectures for Time Critical Mobility Forecasting -- The o Big Data Architecture for Mobility Analytics -- Part VI: Ethical Issues for Time Critical Mobility Analytics -- Ethical Issues in Big Data Analytics for Time Critical Mobility Forecasting.

This book provides detailed descriptions of big data solutions for activity detection and forecasting of very large numbers of moving entities spread across large geographical areas. It presents state-of-the-art methods for processing, managing, detecting and predicting trajectories and important events related to moving entities, together with advanced visual analytics methods, over multiple heterogeneous, voluminous, fluctuating and noisy data streams from moving entities, correlating them with data from archived data sources expressing e.g. entities' characteristics, geographical information, mobility patterns, mobility regulations and intentional data. The book is divided into six parts: Part I discusses the motivation and background of mobility forecasting supported by trajectory-oriented analytics, and includes specific problems and challenges in the aviation (air-traffic management) and the maritime domains. Part II focuses on big data quality assessment and processing, and presents novel technologies suitable for mobility analytics components. Next, Part III describes solutions toward processing and managing big spatio-temporal data, particularly enriching data streams and integrating streamed and archival data to provide coherent views of mobility, and storing of integrated mobility data in large distributed knowledge graphs for efficient query-answering. Part IV focuses on mobility analytics methods exploiting (online) processed, synopsized and enriched data streams as well as (offline) integrated, archived mobility data, and highlights future location and trajectory prediction methods, distinguishing between short-term and more challenging long-term predictions. Part V examines how methods addressing data management, data processing and mobility analytics are integrated in big data architectures with distinctive characteristics compared to other known big data paradigmatic architectures. Lastly, Part VI covers important ethical issues that research on mobility analytics should address. Providing novel approaches and methodologies related to mobility detection and forecasting needs based on big data exploration, processing, storage, and analysis, this book will appeal to computer scientists and stakeholders in various application domains.

ISBN: 9783030451646$q(electronic bk.)

Standard No.: 10.1007/978-3-030-45164-6doiSubjects--Topical Terms:

618526
Transportation
--Data processing.

LC Class. No.: HE147.6

Dewey Class. No.: 388.0285

Big data analytics for time-critical mobility forecastingfrom raw data to trajectory-oriented mobility analytics in the aviation and maritime domains /
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520 $a This book provides detailed descriptions of big data solutions for activity detection and forecasting of very large numbers of moving entities spread across large geographical areas. It presents state-of-the-art methods for processing, managing, detecting and predicting trajectories and important events related to moving entities, together with advanced visual analytics methods, over multiple heterogeneous, voluminous, fluctuating and noisy data streams from moving entities, correlating them with data from archived data sources expressing e.g. entities' characteristics, geographical information, mobility patterns, mobility regulations and intentional data. The book is divided into six parts: Part I discusses the motivation and background of mobility forecasting supported by trajectory-oriented analytics, and includes specific problems and challenges in the aviation (air-traffic management) and the maritime domains. Part II focuses on big data quality assessment and processing, and presents novel technologies suitable for mobility analytics components. Next, Part III describes solutions toward processing and managing big spatio-temporal data, particularly enriching data streams and integrating streamed and archival data to provide coherent views of mobility, and storing of integrated mobility data in large distributed knowledge graphs for efficient query-answering. Part IV focuses on mobility analytics methods exploiting (online) processed, synopsized and enriched data streams as well as (offline) integrated, archived mobility data, and highlights future location and trajectory prediction methods, distinguishing between short-term and more challenging long-term predictions. Part V examines how methods addressing data management, data processing and mobility analytics are integrated in big data architectures with distinctive characteristics compared to other known big data paradigmatic architectures. Lastly, Part VI covers important ethical issues that research on mobility analytics should address. Providing novel approaches and methodologies related to mobility detection and forecasting needs based on big data exploration, processing, storage, and analysis, this book will appeal to computer scientists and stakeholders in various application domains.
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