Synner: Generating Realistic Synthetic Data

Miro Mannino, Azza Abouzied

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish (US)
Title of host publicationSIGMOD 2020 - Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data
PublisherAssociation for Computing Machinery
Pages2749-2752
Number of pages4
ISBN (Electronic)9781450367356
DOIs
StatePublished - Jun 14 2020
Event2020 ACM SIGMOD International Conference on Management of Data, SIGMOD 2020 - Portland, United States
Duration: Jun 14 2020Jun 19 2020

Publication series

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

Conference

Conference2020 ACM SIGMOD International Conference on Management of Data, SIGMOD 2020
CountryUnited States
CityPortland
Period6/14/206/19/20

Keywords

  • data generation
  • declarative languages
  • example-driven interaction

ASJC Scopus subject areas

  • Software
  • Information Systems

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