CBL - Campus del Baix Llobregat

Projecte llegit

Títol: Análisis, uso y desarrollo experimental de herramientas y tecnologías Open Source en Big Data

Estudiants que han llegit aquest projecte:

Director/a: MESEGUER PALLARÈS, ROC

Departament: DAC

Títol: Análisis, uso y desarrollo experimental de herramientas y tecnologías Open Source en Big Data

Data inici oferta: 03-02-2017     Data finalització oferta: 03-10-2017


Estudis d'assignació del projecte:
    GR ENG SIS TELECOMUN
    GR ENG TELEMÀTICA
Tipus: Conjunt     Nombre d'estudiants per realitzar-ho: 1-2
 
Lloc de realització: EETAC
 
Paraules clau:
Big Data, Apache Hadoop, Apache Spark, Lenguaje R, Scala, Machine Learning, Tecnologías y herramientas de Big Data, Comparativa.
 
Descripció del contingut i pla d'activitats:
 
Overview (resum en anglès):
In this project, we pretend to analise, use and justify the use of Big Data nowadays in different areas like companies, research laboratories, etc., as well as different tools and Open Source technologies behind it that make it possible.

The methodology used for this project has consisted on doing an exhaustive analysis of the current and future situation of Big Data, the introduction of the tools and Open Source technologies available, the study and comparative of each of them and the final experimental development with our own data.

We highlight the use of Big Data, along with the associated technologies and tools such as Hadoop and Spark, as a great alternative to classic way of storing and processing big amounts of data.

Given that it is a new concept for us, we are going to try to exploit as much as possible the Open Source functionalities related to Big Data and we are going to give our own personal recommendation on each one evaluating their main characteristics.

Finally, we are going to dig further on the concept of Machine Learning on which a lot of companies are focusing their development. In order to do this, we are going to do an experimental project aiming to detect people’s position based on the access points whose devices are connected to. The main objective is to study different algorithms to obtain the best possible accuracy of personal position and to compare the different results throw the analysis and processing of obtained data such as Building, Floor, etc.

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