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AI Assistants: A Framework for Semi-Automated Data Wrangling

Petricek, T., van den Burg, G. J. J., Nazábal, A. , Ceritli, T., Jimenez-Ruiz, E. ORCID: 0000-0002-9083-4599 & Williams, C. K. I. (2022). AI Assistants: A Framework for Semi-Automated Data Wrangling. IEEE Transactions on Knowledge and Data Engineering, 35(9), pp. 9295-9306. doi: 10.1109/tkde.2022.3222538


Data wrangling tasks such as obtaining and linking data from various sources, transforming data formats, and correcting erroneous records, can constitute up to 80% of typical data engineering work. Despite the rise of machine learning and artificial intelligence, data wrangling remains a tedious and manual task. We introduce AI assistants, a class of semi-automatic interactive tools to streamline data wrangling. An AI assistant guides the analyst through a specific data wrangling task by recommending a suitable data transformation that respects the constraints obtained through interaction with the analyst. We formally define the structure of AI assistants and describe how existing tools that treat data cleaning as an optimization problem fit the definition. We implement AI assistants for four common data wrangling tasks and make AI assistants easily accessible to data analysts in an open-source notebook environment for data science, by leveraging the common structure they follow. We evaluate our AI assistants both quantitatively and qualitatively through three example scenarios. We show that the unified and interactive design makes it easy to perform tasks that would be difficult to do manually or with a fully automatic tool.

Publication Type: Article
Additional Information: ©2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. For the purpose of open access, the authors have applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this.
Publisher Keywords: Data Wrangling, Data Cleaning, Human-in-the-Loop
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TA Engineering (General). Civil engineering (General)
Departments: School of Science & Technology > Computer Science
SWORD Depositor:
[thumbnail of AIDA_AI _Assistants_TKDE_2022.pdf]
Text - Accepted Version
Available under License Creative Commons Attribution.

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