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Title: Real-time scalable parallel data stream classification
Author: Robledo Mcclymont, Roberto Dean
Director: Rodero Castro, Iván
Keywords: big data
High Performance Computing
artificial intelligence
Apache Kafka
deep learning
Issue Date: Jun-2018
Publisher: Universitat Oberta de Catalunya (UOC)
Abstract: The main objective of this final master project is to create a real-time prototype that is capable of classifying real-time data using several deep learning algorithms. Classifying means to give "valuable" information ¿ that maybe can be unknown - to the different incoming data. Note also that this could be extrapolated to other fields. In addition, some research will be done in the field of deep learning with the aim of giving some guidelines about how big data can be classified in a cluster environment. The idea of developing this prototype is to prove that large amounts of data processing can be tackled within this methodology. Further work can be done following this line with the purpose of creating a real data-time analysis methodology that can be applicable to other fields such us medical studies, economic statistics, mobility solutions and many others. As in all research studies, iterative processing must be done in order to enhance and/or update the deep algorithms that will be presented during this final master project.
Language: English
Appears in Collections:Bachelor thesis, research projects, etc.

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