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.
This work has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No [694980] SYNTH: Synthesising Inductive Data Models).
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Notes
- 1.
Demonstration video: https://youtu.be/df6JgHl28Vw.
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Gautrais, C. et al. (2021). VisualSynth: Democratizing Data Science in Spreadsheets. In: Dong, Y., Ifrim, G., Mladenić, D., Saunders, C., Van Hoecke, S. (eds) Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12461. Springer, Cham. https://doi.org/10.1007/978-3-030-67670-4_37
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DOI: https://doi.org/10.1007/978-3-030-67670-4_37
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