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Statistical Analysis of Data from PDF Files
Oltmanová, Kristína ; Burgetová, Ivana (oponent) ; Bartík, Vladimír (vedoucí práce)
This thesis is concerning the process of data extraction from tables from documents in PDF format and their subsequent analysis with the exploitation of statistical methods. The goal of this thesis is to demonstrate the process of obtaining, processing and analyzing data from PDF files, which, in consideration of their program processing, create a finite number of subgroups with common characteristics. Firstly, the reader will become acquainted with the fundamentals of PDF file processing and basic mathematical principles that are required in order to statistically evaluate given data. Obtained theoretical principles are then applied to practical use and programming form in the Python programming language. The resulting web application is programmed using the Flask Python library and is usable on a local server.
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Modern Web Interface of NERD System
Oltmanová, Kristína ; Hranický, Radek (oponent) ; Žádník, Martin (vedoucí práce)
This thesis deals with the issue of designing a modern web interface for an existing system of a database of malicious network entities. The thesis describes the design of a new user interface and also addresses the changes in the existing NERD system, which had to be adapted to the needs of the new web interface. The changes to the existing system were based on expanding the previous functionality in the Python language and the Flask framework. Vue.js (a JavaScript framework) was used to implement the new web interface. This thesis also presents an improved user management with support for external identity providers. The final result of the thesis is a usable modern web interface for the NERD system, which preserves the functionality of the original interface and further extends it.
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Statistical Analysis of Data from PDF Files
Oltmanová, Kristína ; Burgetová, Ivana (oponent) ; Bartík, Vladimír (vedoucí práce)
This thesis is concerning the process of data extraction from tables from documents in PDF format and their subsequent analysis with the exploitation of statistical methods. The goal of this thesis is to demonstrate the process of obtaining, processing and analyzing data from PDF files, which, in consideration of their program processing, create a finite number of subgroups with common characteristics. Firstly, the reader will become acquainted with the fundamentals of PDF file processing and basic mathematical principles that are required in order to statistically evaluate given data. Obtained theoretical principles are then applied to practical use and programming form in the Python programming language. The resulting web application is programmed using the Flask Python library and is usable on a local server.
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