Miro Mannino
ABSTRACT
Synner allows users to generate realistic-looking data. With Synner users can visually and declaratively specify properties of the dataset they wish to generate. Such properties include the domain, and statistical distribution of each field, and relationships between fields. User can also sketch custom distributions and relationships. Synner provides instant feedback on every user interaction by visualizing a preview of the generated data. It also suggests generation specifications from a few user-provided examples of data to generate, column labels and other user interactions. In this demonstration, we showcase Synner and summarize results from our evaluation of Synner's effectiveness at generating realistic-looking data.
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- Miro Mannino and Azza Abouzied. 2019. Is This Real? Generating Synthetic Data That Looks Real. In Proceedings of the 32nd Annual ACM Symposium on User Interface Software and Technology (New Orleans, LA, USA) (UIST '19). Association for Computing Machinery, New York, NY, USA, 549--561. https://doi.org/10.1145/3332165.3347866Google ScholarDigital Library
Index Terms
- Synner: Generating Realistic Synthetic Data
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