Publication at Faculty of Mathematics and Physics |
2016
Abstract
This paper describes a tool for generation of synthetic semi-structured JSON Big Data, called JBD generator. Its main focus is on parallel execution of the generation process while preserving the ability to control the contents of the generated documents.
It can also accept samples of real-world data characterizing the target synthetic data and is also capable of automatic creation of references between JSON documents. The results of experiments with the data generator exploited for the purpose of testing database MongoDB describe its added value.