Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/72262
Título: Traffic predictive analysis through data stream mining
Autores/as: Guerra Montenegro, Juan Antonio 
Sánchez-Medina, Javier J. 
Clasificación UNESCO: 120304 Inteligencia artificial
Palabras clave: Data science
Data stream mining
Predictive analysis
Traffic modeling
Fecha de publicación: 2020
Editor/a: Springer
Publicación seriada: Lecture Notes in Computer Science 
Conferencia: International Conference on Computer Aided Systems Theory (EUROCAST 2019) 
Resumen: With a huge increase in computational power, Traffic Predictive Analysis has seen various improvements in the recent years. Additionally, this field is experimenting an increase in available data, which allows to produce more precise forecasting and classification models. However, this means that the available data has also seen a huge increase in terms of storage size. Data Stream Mining provides a brand new approach to data processing, allowing to create adaptive, incremental models that do not need huge amounts of storage size, as the data is processed as it is received. In this communication, we will explore the state of the art and the first research efforts that can be found in this direction.
URI: http://hdl.handle.net/10553/72262
ISBN: 978-3-030-45095-3
ISSN: 0302-9743
DOI: 10.1007/978-3-030-45096-0_24
Fuente: Computer Aided Systems Theory – EUROCAST 2019. EUROCAST 2019. Lecture Notes in Computer Science, v. 12014 LNCS, p. 190-196, (Enero 2020)
Colección:Capítulo de libro
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