1. Comparison of Approaches to Synthetic Data Generation
- Author
-
Šejvlová, Ludmila, Šimůnek, Milan, and Pavlíčková, Jarmila
- Subjects
Náhodný přístup ,Synthpop ,Anonymizace dat ,Umělá data ,Synthetic data ,Approaches to synthetic data generation ,Synthetic data generation process ,ReverseMiner ,Testování softwaru ,Synthetic Data Definition Language ,Statistický přístup ,LISp-Miner ,Generovací jazyky ,Generation languages ,Software testing ,Mockaroo ,Random approach ,Přístupy ke generování umělých dat ,GUHA-procedury ,Education data ,Proces generování umělých dat ,GUHA-procedures ,Statistic approach ,Výuková data ,Data anonymization - Abstract
The diploma thesis deals with synthetic data, selected approaches to their generation together with a practical task of data generation. The goal of the thesis is to describe the selected approaches to data generation, capture their key advantages and disadvantages and compare the individual approaches to each other. The practical part of the thesis describes generation of synthetic data for teaching knowledge discovery using databases. The thesis includes a basic description of synthetic data and thoroughly explains the process of their generation. The approaches selected for further examination are random data generation, the statistical approach, data generation languages and the ReverseMiner tool. The thesis also describes the practical usage of synthetic data and the suitability of each approach for certain purposes. Within this thesis, educational data Hotel SD were created using the ReverseMiner tool. The data contain relations discoverable with SD (set-difference) GUHA-procedures.
- Published
- 2017