@article{FERNANDEZ20,
title = {Automated Deployment of a Spark Cluster with Machine Learning Algorithm Integration},
author = {A. M. Fernández and D. Gutiérrez-Avilés and A. Troncoso and F. Martínez-Álvarez},
url = {https://www.sciencedirect.com/science/article/pii/S2214579620300034},
doi = {10.1016/j.bdr.2020.100135},
year = {2020},
date = {2020-05-12},
journal = {Big Data Research},
volume = {19-20},
pages = {100135},
abstract = {The vast amount of data stored nowadays has turned big data analytics into a very trendy research field. The Spark distributed computing platform has emerged as a dominant and widely used paradigm for cluster deployment and big data analytics. However, to get started up is still a task that may take much time when manually done, due to the requisites that all nodes must fulfill. This work introduces LadonSpark, an open-source and non-commercial solution to configure and deploy a Spark cluster automatically. It has been specially designed for easy and efficient management of a Spark cluster with a friendly graphical user interface to automate the deployment of a cluster and to start up the distributed file system of Hadoop quickly. Moreover, LadonSpark includes the functionality of integrating any algorithm into the system. That is, the user only needs to provide the executable file and the number of required inputs for proper parametrization. Source codes developed in Scala, R, Python, or Java can be supported on LadonSpark. Besides, clustering, regression, classification, and association rules algorithms are already integrated so that users can test its usability from its initial installation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@conference{SOCO2019,
title = {Real-Time Big Data Analytics in Smart Cities from LoRa-based IoT Networks},
author = {A. M. Fernández and D. Gutiérrez-Avilés and A. Troncoso and F. Martínez-Álvarez},
url = {https://link.springer.com/chapter/10.1007/978-3-030-20055-8_9},
year = {2019},
date = {2019-01-01},
booktitle = {SOCO 14th International Conference on Soft Computing Models in Industrial and Environmental Applications},
series = {Advances in Intelligent Systems and Computing},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}

@conference{TAMIDA2016,
title = {Automated Spark clusters deployment for Big Data with standalone applications integration},
author = {A. M. Fernández and J. F. Torres and A. Troncoso and F. Martínez-Álvarez},
url = {https://link.springer.com/chapter/10.1007/978-3-319-44636-3_14},
year = {2016},
date = {2016-01-01},
booktitle = {CAEPIA Multiconferencia de la Asociación Española para la Inteligencia Artificial (TAMIDA VII Simposio de Teoría y Aplicaciones de Minería de Datos)},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}