Complaint-driven Training Data Debugging for Query 2.0

@article{Wu2020ComplaintdrivenTD,
  title={Complaint-driven Training Data Debugging for Query 2.0},
  author={Weiyuan Wu and Lampros Flokas and Eugene Wu and Jiannan Wang},
  journal={Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data},
  year={2020}
}
  • Weiyuan Wu, Lampros Flokas, +1 author Jiannan Wang
  • Published 2020
  • Computer Science
  • Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data
  • As the need for machine learning (ML) increases rapidly across all industry sectors, there is a significant interest among commercial database providers to support "Query 2.0", which integrates model inference into SQL queries. Debugging Query 2.0 is very challenging since an unexpected query result may be caused by the bugs in training data (e.g., wrong labels, corrupted features). In response, we propose Rain, a complaint-driven training data debugging system. Rain allows users to specify… CONTINUE READING

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