Related papers: Human-Machine Collaboration for Democratizing Data Science

Human-Machine Collaboration for Democratizing Data Science

URL: http://arxiv.org/abs/2004.11113v1
Date: Thu, 23 Apr 2020 12:50:52 GMT
Title: Human-Machine Collaboration for Democratizing Data Science
Authors: Cl\'ement Gautrais, Yann Dauxais, Stefano Teso, Samuel Kolb, Gust Verbruggen, Luc De Raedt
Abstract summary: textscVisualSynth relies on the user providing colored sketches, i.e., coloring parts of the spreadsheet, to partially specify data science tasks. It performs various data analysis tasks ranging from data wrangling, data selection, clustering, constraint learning, predictive modeling and auto-completion.
Score: 23.385646192087922
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: Everybody wants to analyse their data, but only few posses the data science expertise to to this. Motivated by this observation we introduce a novel framework and system \textsc{VisualSynth} for human-machine collaboration in data science. It wants to democratize data science by allowing users to interact with standard spreadsheet software in order to perform and automate various data analysis tasks ranging from data wrangling, data selection, clustering, constraint learning, predictive modeling and auto-completion. \textsc{VisualSynth} relies on the user providing colored sketches, i.e., coloring parts of the spreadsheet, to partially specify data science tasks, which are then determined and executed using artificial intelligence techniques.

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