Feedback Driven Improvement of Data Preparation Pipelines
- Submitting institution
-
The University of Manchester
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 157935887
- Type
- D - Journal article
- DOI
-
10.1016/j.is.2019.101480
- Title of journal
- Information Systems
- Article number
- 101480
- First page
- -
- Volume
- 92
- Issue
- 0
- ISSN
- 0306-4379
- Open access status
- Exception within 3 months of publication
- Month of publication
- December
- 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
-
1
- Research group(s)
-
A - Computer Science
- Citation count
- 0
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- "Data scientists are reported to spend 80% of their time authoring data preparation pipelines. Authoring such pipelines is amenable to automation, but automatically generated pipelines may not be correct first time, and thus need to be revised in the light of feedback. Although feedback has been widely investigated in data preparation, this paper is significant in presenting the first approach in which a single type of feedback is applied to inform coordinated actions across many data preparation steps.
Techniques from the paper have been included in Data Preparer, commercialised by The Data Value Factory (https://thedatavaluefactory.com)."
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
