Development of an ETL- and Machine Leading System for Loading and Analyzing Large-Volume Data in a Common Data Warehouse

Student: Chaynikov Anton

Supervisor: Aleksei Fomin

Educational Programme: Business Informatics (Bachelor)

Final Grade: 9

Year of Graduation: 2016

This study aims to explore instruments of machine learning, to apply them to replication and analysis of a large set of textual, meta- and visual data, and to construct a robust auto-tagging system for this set. Employed tools are Python scientific data analysis stack and Caffe, a set machine vision libraries focused on the development of neural networks. Results include full extraction of metadata and partial extraction of visual data, profound experience in installation of caffe, and a set of different classifiers trained on various subsets of replicated data, including several instances of neural net of CaffeNet architecture.

Full text (added May 19, 2016)

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