VisualSynth: Democratizing Data Science in Spreadsheets
Abstract
We introduce VisualSynth , a framework that wants to democratize data science by enabling naive end-users to specify the data science tasks that match their needs. In VisualSynth , the user and the spreadsheet application interact by highlighting parts of the data using colors . The colors define a partial specification of a data science task (such as data wrangling or clustering), which is then completed and solved automatically using artificial intelligence techniques. The user can interactively refine the specification until she is satisfied with the result.
Cite
Text
Gautrais et al. "VisualSynth: Democratizing Data Science in Spreadsheets." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2020. doi:10.1007/978-3-030-67670-4_37Markdown
[Gautrais et al. "VisualSynth: Democratizing Data Science in Spreadsheets." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2020.](https://mlanthology.org/ecmlpkdd/2020/gautrais2020ecmlpkdd-visualsynth/) doi:10.1007/978-3-030-67670-4_37BibTeX
@inproceedings{gautrais2020ecmlpkdd-visualsynth,
title = {{VisualSynth: Democratizing Data Science in Spreadsheets}},
author = {Gautrais, Clément and Dauxais, Yann and Kolb, Samuel and Jain, Arcchit and Kumar, Mohit and Teso, Stefano and Van Wolputte, Elia and Verbruggen, Gust and De Raedt, Luc},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2020},
pages = {550-554},
doi = {10.1007/978-3-030-67670-4_37},
url = {https://mlanthology.org/ecmlpkdd/2020/gautrais2020ecmlpkdd-visualsynth/}
}