Lessons for supporting data science from the everyday automation experience of spell-checkers

Research output: Contribution to journalConference articlepeer-review

Abstract

We apply two theoretical frameworks to analyze spell-checkers as a form of automation and apply the lessons learned to analyze opportunities to support data science. The analysis distinguishes between automation of analysis to suggest actions and automation of implementation of actions. Having the automation work in the same space as users (e.g., editing the same document) supports stigmergic coordination between the two, but attention is needed to ensure that the contributions can be combined and have a recognizable form that indicates their purpose.

Original languageEnglish (US)
JournalCEUR Workshop Proceedings
Volume2700
StatePublished - 2020
Event2020 Workshop on Automation Experience across Domains, AutomationXP 2020 - Honolulu, United States
Duration: Apr 26 2020 → …

Keywords

  • Automation
  • Spell-checking

ASJC Scopus subject areas

  • Computer Science(all)

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