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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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Mobility Analytics for Spatio-Temporal and Social Data - First International Workshop, MATES 2017, Munich, Germany, September 1, 2017, Revised Selected Papers (Paperback, 1st ed. 2018)
Christos Doulkeridis, George A. Vouros, Qiang Qu, Shuhui Wang
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R1,684
Discovery Miles 16 840
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Ships in 10 - 15 working days
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This book constitutes the refereed post-conference proceedings of
the First International Workshop on Mobility Analytics for
Spatio-Temporal and Social Data, MATES 2017, held in Munich,
Germany, in September 2017. The 6 revised full papers and 2 short
papers included in this volume were carefully reviewed and selected
from 13 submissions. Also included are two keynote speeches. The
papers intend to raise awareness of real-world problems in critical
domains which require novel data management solutions. They are
organized in two thematic sections: social network analytics and
applications, and spatio-temporal mobility analytics.
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.
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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