LEADER 06594nam 22006615 450 001 996465364603316 005 20200706091249.0 010 $a3-030-45164-X 024 7 $a10.1007/978-3-030-45164-6 035 $a(CKB)5310000000016580 035 $a(MiAaPQ)EBC6235689 035 $a(DE-He213)978-3-030-45164-6 035 $a(PPN)248598163 035 $a(EXLCZ)995310000000016580 100 $a20200623d2020 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBig Data Analytics for Time-Critical Mobility Forecasting$b[electronic resource] $eFrom Raw Data to Trajectory-Oriented Mobility Analytics in the Aviation and Maritime Domains /$fedited by George A. Vouros, Gennady Andrienko, Christos Doulkeridis, Nikolaos Pelekis, Alexander Artikis, Anne-Laure Jousselme, Cyril Ray, Jose Manuel Cordero, David Scarlatti 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (378 pages) 311 $a3-030-45163-1 327 $aPart 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 ? 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. 330 $aThis 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. 606 $aDatabase management 606 $aMathematical statistics 606 $aTransportation engineering 606 $aTraffic engineering 606 $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024 606 $aProbability and Statistics in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/I17036 606 $aTransportation Technology and Traffic Engineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T23120 615 0$aDatabase management. 615 0$aMathematical statistics. 615 0$aTransportation engineering. 615 0$aTraffic engineering. 615 14$aDatabase Management. 615 24$aProbability and Statistics in Computer Science. 615 24$aTransportation Technology and Traffic Engineering. 676 $a385.0724 702 $aVouros$b George A$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aAndrienko$b Gennady$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aDoulkeridis$b Christos$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aPelekis$b Nikolaos$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aArtikis$b Alexander$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aJousselme$b Anne-Laure$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRay$b Cyril$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aCordero$b Jose Manuel$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aScarlatti$b David$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996465364603316 996 $aBig Data Analytics for Time-Critical Mobility Forecasting$92224677 997 $aUNISA