|Title:||Traffic predictive analysis through data stream mining||Authors:||Guerra Montenegro, Juan Antonio
Sánchez-Medina, Javier J.
|UNESCO Clasification:||120304 Inteligencia artificial||Keywords:||Data science
Data stream mining
|Issue Date:||2020||Publisher:||Springer||Journal:||Lecture Notes in Computer Science||Conference:||International Conference on Computer Aided Systems Theory (EUROCAST 2019)||Abstract:||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||Source:||Computer Aided Systems Theory – EUROCAST 2019. EUROCAST 2019. Lecture Notes in Computer Science, v. 12014 LNCS, p. 190-196, (Enero 2020)|
|Appears in Collections:||Capítulo de libro|
checked on Feb 18, 2023
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