Big Data Analytics for Time-Critical Mobility Forecasting, 1st ed. 2020 From Raw Data to Trajectory-Oriented Mobility Analytics in the Aviation and Maritime Domains

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.

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 δ 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.

George Vouros is a Professor at the Department of Digital Systems, University of Piraeus, where he also heads the AI Lab. He has conducted extensive research in the areas of expert systems, knowledge management, knowledge representation and reasoning with ontologies, multi-agent systems and reinforcement learning. He also coordinated the datAcron Big Data project (H2020 ICT-16), on which this book is based.

Dr. Gennady Andrienko is a lead scientist responsible for visual analytics research at the Fraunhofer Institute IAIS (Sankt Augustin, Germany) and Full Professor at City University London. He has co-authored two monographs “Exploratory Analysis of Spatial and Temporal Data” (Springer, 2006) and “Visual Analytics of Movement” (Springer, 2013) and ca. 100 peer-reviewed journal papers. Gennady Andrienko received the Test of Time award at IEEE VAST 2018 and best paper awards at the AGILE 2006, IEEE VAST 2011 and 2012 conferences and EuroVA 2018 workshop.

Christos Doulkeridis is an Assistant Professor at the Department of Digital Systems at the University of Piraeus. He had been awarded both a Marie Curie fellowship and an ERCIM “Allain Bensoussan” fellowship for post-doctoral studies at the Norwegian University of Science and Technology. His research interests include big data management, parallel and distributed query processing, as well as indexing of spatial, spatio-temporal, and spatio-textual data.

Nikolaos Pelekis is an Associate Professor at the Department of Statistics and Insurance Science, University of Piraeus, Greece. His research interests include all aspects of data science, particularly mobility data management and mining. Nikos has co-authored one monograph and more than 100 refereed articles in scientific journals and conference proceedings, has received three best paper awards and won the SemEval’17 competition.

Alexander Artikis is an Assistant Professor at the Univ