Incorporating Data Context to Cost-Effectively Automate End-to-End Data Wrangling
- Submitting institution
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The University of Manchester
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 132168537
- Type
- D - Journal article
- DOI
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10.1109/TBDATA.2019.2907588
- Title of journal
- IEEE Transactions on Big Data
- Article number
- -
- First page
- 1
- Volume
- 0
- Issue
- 0
- ISSN
- 2332-7790
- Open access status
- Compliant
- Month of publication
- April
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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9
- Research group(s)
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A - Computer Science
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "Data scientists are widely reported to be spending 80% of their time on data wrangling; integrating and cleaning data prior to analysis. This paper is significant in presenting an approach to automating the generation of data preparation programs.
Keynote at 21st International Workshop on Design, Optimisation, Languages and Analytical Processing of Big Data, Lisbon, Portugal, March 2019.
The approach has been commercialised by a Manchester spinout company, The Data Value Factory (https://thedatavaluefactory.com)."
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
