The phrase "data generator" is correct and usable in written English.
It can be used when referring to a tool or system that produces data, often in the context of software, research, or data analysis.
Example: "The data generator created a large dataset for our machine learning model."
Alternatives: "data producer" or "data creator."
Empirical results show that our test data generator is effective in generating, in one run, multiple test data to cover multiple target paths.
The synthetic data generator GeneNetWeaver uses topologies generated based on real biological networks, therefore allowing good approximation of the statistical properties of real biological networks.
XMark 2 is an XML benchmark dataset generated using the data generator with factor = 0.05.
T10I4D100K (artificial datasets generated by IBM's data generator) [37] have (10^5) transactions with 870 items in it.
Datasets S1 and S2 are synthetic datasets which we generated applying the RDG1 data generator in WEKA [36] data mining tool with default parameter settings.
The synthetic data was generated using an amended version of the FEBRL data generator [ 37].
A method complementary to our approach is described by Harman et al. Testability transformation results in a transformed program to be used by a test-data generator to improve its ability of generating test data for the original program [22].
Using a model-based or source-based test-data generator raises the challenge of ensuring that adequate structural code-coverage has been achieved at machine-code level [9].
On the other side, a test-data generator operating on such a high-level program representation could be easily retargeted to different platforms.
Big Data generator suite (BDGS) has been developed for generating scalable big data while employing data models derived from real data [10].
Therefore, we adopt IBM Synthetic Data Generator to generate user's watching habits data.
