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http://hdl.handle.net/10609/65285
Title: | Real-Time behavioural stream analysis with Big Data stack technologies |
Author: | Puertas Ballesteros, Pedro |
Tutor: | Andrés Sanz, Humberto |
Others: | Universitat Oberta de Catalunya |
Abstract: | In this report shows the creation of a Big Data solution using the technologies of AWS Kinesis and Spark Streaming and for the visualization part was used Looker. The objective is to be able validate the activity of the users through the action inside the application of Wallapop. To be able to validate the user activity I made a matrix of transitions using the Hidden Markov Model, this model calculate the probability between the actions of the user. Finally, it is to pre-block the user validating the activity and make a decision. |
Keywords: | Big data machine learning business intelligence Apache Hadoop wallapop |
Document type: | info:eu-repo/semantics/bachelorThesis |
Issue Date: | 30-Jun-2017 |
Publication license: | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
Appears in Collections: | Bachelor thesis, research projects, etc. |
Files in This Item:
File | Description | Size | Format | |
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Puertas_Ballesteros_Pedro_MEMO¿RIA.docx | 56,93 MB | Microsoft Word XML | View/Open | |
Puertas_Ballesteros_Pedro_PRESENTACIO¿.pptx | 11,27 MB | Microsoft Powerpoint XML | View/Open | |
ppuertasbTFG0617memoria.pdf | Memoria del TFG | 2,9 MB | Adobe PDF | View/Open |
ppuertasbTFG0617presentación.pdf | Presentación del TFG | 1,07 MB | Adobe PDF | View/Open |
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