Putting Things into Context: Rich Explanations for Query Answers using Join Graphs

Authors

Materials

Abstract

In many data analysis applications there is a need to explain why a surprising or interesting result was produced by a query. Previous approaches to explaining results have directly or indirectly relied on data provenance, i.e., input tuples contributing to the result(s) of interest. However, some information that is relevant for explaining an answer may not be contained in the provenance. We propose a new approach for explaining query results by augmenting provenance with information from other related tables in the database. Using a suite of optimization techniques, we demonstrate experimentally using real datasets and through a user study that our approach produces meaningful results and is efficient.

bibtex

@inproceedings{LM21,
  author = {Li, Chenjie and Miao, Zhengjie and Zeng, Qitian and Glavic, Boris and Roy, Sudeepa},
  booktitle = {Proceedings of the 46th International Conference on Management of Data},
  pages = {1051–1063},
  projects = {Explanations beyond Provenance},
  title = {Putting Things into Context: Rich Explanations for Query Answers using Join Graphs},
  pdfurl = {https://dl.acm.org/doi/pdf/10.1145/3448016.3459246},
  doi = {10.1145/3448016.3459246},
  keywords = {Provenance; Explanations},
  venueshort = {SIGMOD},
  reproducibility = {https://github.com/IITDBGroup/CaJaDe},
  video = {https://www.youtube.com/watch?v=puhCAnFuPR4&list=PL3xUNnH4TdbsfndCMn02BqAAgGB0z7cwq},
  longversionurl = {https://arxiv.org/pdf/2103.15797},
  year = {2021}
}

Reference

Putting Things into Context: Rich Explanations for Query Answers using Join Graphs Chenjie Li, Zhengjie Miao, Qitian Zeng, Boris Glavic and Sudeepa Roy Proceedings of the 46th International Conference on Management of Data (2021), pp. 1051–1063.