Name
Abstract | ||
|---|---|---|
Datasets of different characteristics are needed by the research community for experimental purposes. However, real data may be difficult to obtain due to privacy concerns. Moreover, real data may not meet specific characteristics which are needed to verify new approaches under certain conditions. Given these limitations, the use of synthetic data is a viable alternative to complement the real data. In this report, we describe the process followed to generate synthetic data using Benerator, a publicly available tool. The results show that the synthetic data preserves a high level of accuracy compared to the original data. The generated datasets correspond to microdata containing records with social, economic and demographic data which mimics the distribution of aggregated statistics from the 2011 Irish Census data. |
Year | Venue | Field |
|---|---|---|
2013 | CoRR | Data mining,Synthetic data generation,Computer science,Synthetic data,Microdata (HTML),Technical report |
DocType | Volume | Citations |
Journal | abs/1311.3312 | 3 |
PageRank | References | Authors |
0.47 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vanessa Ayala-Rivera | 1 | 18 | 3.96 |
| Patrick McDonagh | 2 | 38 | 5.11 |
| Thomas Cerqueus | 3 | 45 | 10.23 |
| Liam Murphy | 4 | 7 | 1.61 |