Synner: Generating Realistic Synthetic Data

Miro Mannino; Azza Abouzied

doi:10.1145/3318464.3384696

Synner: Generating Realistic Synthetic Data

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English (US)
Title of host publication	SIGMOD 2020 - Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data
Publisher	Association for Computing Machinery
Pages	2749-2752
Number of pages	4
ISBN (Electronic)	9781450367356
DOIs	https://doi.org/10.1145/3318464.3384696
State	Published - Jun 14 2020
Event	2020 ACM SIGMOD International Conference on Management of Data, SIGMOD 2020 - Portland, United States Duration: Jun 14 2020 → Jun 19 2020

Publication series

Name	Proceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)	0730-8078

Conference

Conference	2020 ACM SIGMOD International Conference on Management of Data, SIGMOD 2020
Country/Territory	United States
City	Portland
Period	6/14/20 → 6/19/20

Keywords

data generation
declarative languages
example-driven interaction

ASJC Scopus subject areas

Software
Information Systems

Access to Document

10.1145/3318464.3384696

Cite this

Synner: Generating Realistic Synthetic Data. / Mannino, Miro; Abouzied, Azza.
SIGMOD 2020 - Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. Association for Computing Machinery, 2020. p. 2749-2752 (Proceedings of the ACM SIGMOD International Conference on Management of Data).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Mannino, M & Abouzied, A 2020, Synner: Generating Realistic Synthetic Data. in SIGMOD 2020 - Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data. Proceedings of the ACM SIGMOD International Conference on Management of Data, Association for Computing Machinery, pp. 2749-2752, 2020 ACM SIGMOD International Conference on Management of Data, SIGMOD 2020, Portland, United States, 6/14/20. https://doi.org/10.1145/3318464.3384696

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Synner: Generating Realistic Synthetic Data

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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