@misc{10481/99224,
year = {2019},
url = {https://hdl.handle.net/10481/99224},
abstract = {The amount of information generated in social media channels or economical/business transactions
exceeds the usual bounds of static databases and is in continuous growing. In this work, we propose a
frequent itemset mining method using sliding windows capable of extracting tendencies from continuous
data flows. For that aim, we develop this method using Big Data technologies, in particular, using the Spark
Streaming framework enabling distributing the computation along several clusters and thus improving
the algorithm speed. The experimentation carried out shows the capability of our proposal and its
scalability when massive amounts of data coming from streams are taken into account.},
title = {Finding tendencies in streaming data using big data frequent itemset mining},
doi = {https://doi.org/10.1016/j.knosys.2018.09.026},
author = {Fernández Basso, Carlos Jesús and Francisco Agra, Abel J. and Ruiz Jiménez, María Dolores and Martín Bautista, María José},
}