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http://hdl.handle.net/10609/83465
Title: | Real-time scalable parallel data stream classification |
Author: | Robledo Mcclymont, Roberto Dean |
Tutor: | Rodero, Ivan |
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. |
Keywords: | big data High Performance Computing artificial intelligence Apache Kafka deep learning |
Document type: | info:eu-repo/semantics/masterThesis |
Issue Date: | Jun-2018 |
Publication license: | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
Appears in Collections: | Trabajos finales de carrera, trabajos de investigación, etc. |
Files in This Item:
File | Description | Size | Format | |
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rrobledomTFM0618memoria.pdf | TFM memory | 3,78 MB | Adobe PDF | View/Open |
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