George A. Vouros

Gennady Andrienko

Christos Doulkeridis

Nikolaos Pelekis

Alexander Artikis

Anne-Laure Jousselme

Cyril Ray

Jose Manuel Cordero

David Scarlatti

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.

From Raw Data to Trajectory-Oriented Mobility Analytics in the Aviation and Maritime Domains

Big Data Analytics for Time-Critical Mobility Forecasting

Themistoklis Panayiotopoulos

Arti?cial intelligence has attracted a renewed interest from distinguished sci- tists and has again raised new, more realistic this time, expectations for future advances regarding the development of theories, models and techniques and the use of them in applications pervading many areas of our daily life. The borders of human-level intelligence are still very far away and possibly unknown. Nev- theless, recent scienti?c work inspires us to work even harder in our exploration of the unknown lands of intelligence. This volume contains papers selected for presentation at the 3rd Hellenic Conference on Arti?cial Intelligence (SETN 2004), the o?cial meeting of the Hellenic Society for Arti?cial Intelligence (EETN). The ?rst meeting was held in the University of Piraeus, 1996 and the second in the Aristotle University of Thessaloniki (AUTH), 2002. SETN conferences play an important role in the dissemination of the in- vative and high-quality scienti?c results in arti?cial intelligence which are being produced mainly by Greek scientists in institutes all over the world. However, the most important e?ect of SETN conferences is that they provide the context in which people meet and get to know each other, as well as a very good opp- tunity for students to get closer to the results of innovative arti?cial intelligence research.

Third Helenic Conference on AI, SETN 2004, Samos, Greece, May 5-8, 2004, Proceedings

Methods and Applications of Artificial Intelligence

George A. Vouros

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