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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.
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.
Applications that require a high degree of distribution and
loosely-coupled connectivity are ubiquitous in various domains,
including scientific databases, bioinformatics, and multimedia
retrieval. In all these applications, data is typically voluminous
and multidimensional, and support for advanced query operators is
required for effective querying and efficient processing. To
address this challenge, we adopt a hybrid P2P architecture and
propose novel indexing and query processing algorithms. We present
a scalable framework that relies on data summaries that are
distributed and maintained as multidimensional routing indices.
Different types of data summaries enable efficient processing of a
variety of advanced query operators.
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Discovery Miles 8 890
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