Abstract

Data transformation often requires users to write many trivial and task-dependent programs to transform thousands of records. Recently, programming-by-example (PBE) approaches enable users to transform data without coding. A key challenge of these PBE approaches is to deliver correctly transformed results on large datasets, since 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, because the transformation scenarios are highly task-dependent, existing approaches cannot capture the incorrect records for various scenarios. We present a approach that learns from past transformation scenarios to generate a meta-classifier to identify the incorrect records. Our approach color-codes these transformed …

Date

March 7, 2016

Authors

Bo Wu, Craig A Knoblock

Book

Proceedings of the 21st International Conference on Intelligent User Interfaces

Pages

375-384