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Data Mining with Python
Krestianková, Tamara ; Burgetová, Ivana (referee) ; Zendulka, Jaroslav (advisor)
This thesis deals with principles of data mining process, available Python packages for data mining and a demonstration of Python script capable of data analyisis focused on classification techniques. Created classifiers are able to classify subjects into two groups - healthy people and people suffering from Parkinson's disease - based on their biomedical vocal analysis data.
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Big Data Processing in Industry 4.0
Stredánsky, Dávid ; Fajčík, Martin (referee) ; Smrž, Pavel (advisor)
Main goal of this thesis is to create application for industrial big data processing. Final application uses bearing vibration data. The application's design is inspired by Lambda architecture for big data processing. The application monitors data recieved from sensors in real time and enables periodic batch processing. Known methods from bearing condition monitoring, such as root mean square, deviation or skewness extraction are used in batch processing. Data prediction method Prophet is tested out in this thesis. Final web appli- cation is written in the Python language with the use of Dash framework and results are stored in MySQL database.
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Benchmarking and Profiling of CloverETL Data Processing System
Botka, Roland ; Bartík, Vladimír (referee) ; Rychlý, Marek (advisor)
This thesis deals with the benchmarking and profiling of CloverETL Data Processing system. The aim of this thesis is to design a way of measuring performance and profiling in different runtime environments. The problem was solved by designing and implementing an application, which is based on measured data and its analysis, detects maximum system perpetuity and monitor performance changes in system performance. The advantage is the ability to compare environments on which the CloverETL System is running and application usage in automated testing in company. Created ETL Graphs have been tested during development and they are part of the result of this thesis.
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Big Data Processing in Industry 4.0
Stredánsky, Dávid ; Fajčík, Martin (referee) ; Smrž, Pavel (advisor)
Main goal of this thesis is to create application for industrial big data processing. Final application uses bearing vibration data. The application's design is inspired by Lambda architecture for big data processing. The application monitors data recieved from sensors in real time and enables periodic batch processing. Known methods from bearing condition monitoring, such as root mean square, deviation or skewness extraction are used in batch processing. Data prediction method Prophet is tested out in this thesis. Final web appli- cation is written in the Python language with the use of Dash framework and results are stored in MySQL database.
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Data Mining with Python
Krestianková, Tamara ; Burgetová, Ivana (referee) ; Zendulka, Jaroslav (advisor)
This thesis deals with principles of data mining process, available Python packages for data mining and a demonstration of Python script capable of data analyisis focused on classification techniques. Created classifiers are able to classify subjects into two groups - healthy people and people suffering from Parkinson's disease - based on their biomedical vocal analysis data.
|
|
|
Benchmarking and Profiling of CloverETL Data Processing System
Botka, Roland ; Bartík, Vladimír (referee) ; Rychlý, Marek (advisor)
This thesis deals with the benchmarking and profiling of CloverETL Data Processing system. The aim of this thesis is to design a way of measuring performance and profiling in different runtime environments. The problem was solved by designing and implementing an application, which is based on measured data and its analysis, detects maximum system perpetuity and monitor performance changes in system performance. The advantage is the ability to compare environments on which the CloverETL System is running and application usage in automated testing in company. Created ETL Graphs have been tested during development and they are part of the result of this thesis.
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