DIFF: A Relational Interface for Large-Scale Data Explanation
Summary: DIFF introduces a relational aggregation operator that unifies explanation engines with declarative SQL for large-scale analytics. Implemented in MB SQL (MacroBase), with single-node and distributed deployments, it preserves semantics, enables optimizations, and yields up to 10x speedups in production. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Firas Abuzaid (Stanford University)
- 2. Peter Kraft (Stanford University)
- 3. Sahaana Suri (Stanford University)
- 4. Edward Gan (Stanford University)
- 5. Eric Xu (Stanford University)
- 6. Atul Shenoy (Microsoft)
- 7. Asvin Ananthanarayan (Microsoft)
- 8. John Sheu (Microsoft)
- 9. Erik Meijer (Meta)
- 10. Xi Wu (Google)
- 11. Jeff Naughton (Google)
- 12. Peter Bailis (Stanford University)
- 13. Matei Zaharia (Stanford University)
BibTeX Citation
@article{abuzaid_vldb19,
title = {{DIFF: A Relational Interface for Large-Scale Data Explanation}},
author = {Abuzaid, Firas and Kraft, Peter and Suri, Sahaana and Gan, Edward and Xu, Eric and Shenoy, Atul and Ananthanarayan, Asvin and Sheu, John and Meijer, Erik and Wu, Xi and Naughton, Jeff and Bailis, Peter and Zaharia, Matei},
journal = {PVLDB},
series = {{VLDB} '19},
volume = {12},
number = {4},
pages = {419--432},
doi = {10.14778/3297753.3297761},
url = {https://doi.org/10.14778/3297753.3297761},
year = {2019}
}
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