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IUI
2016
ACM

Maximizing Correctness with Minimal User Effort to Learn Data Transformations

7 years 11 months ago
Maximizing Correctness with Minimal User Effort to Learn Data Transformations
Data transformation often requires users to write many trivial and task-dependent programs to transform thousands of records. Recently, programming-by-example approaches enable users to transform data without coding. A key challenge of these PBE approaches is to deliver correctly transformed results on large datasets, as these transformation programs are likely to be generated by non-expert users. To address this challenge, existing approaches aim to identify a small set of potentially incorrect records and ask users to examine these records instead of the entire dataset. However, as the transformation scenarios are highly task-dependent, existing approaches cannot capture the incorrect records for various scenarios. In this paper, our approach learns from past transformation scenarios to generate a meta-classifier to identify the incorrect records. Our approach color-codes these transformed records and then presents them for users to examine. The approach allows users to either ente...
Bo Wu, Craig A. Knoblock
Added 06 Apr 2016
Updated 06 Apr 2016
Type Journal
Year 2016
Where IUI
Authors Bo Wu, Craig A. Knoblock
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