Efficient Insights Discovery through Conditional Generative Model based Query Approximation
Summary: Electra integrates data-insight discovery with an ML-driven approximate query processor for rapid, time-critical insights and no-code exploration. An ML-driven AQP uses a conditional generative model to synthesize ~1000-row samples, answering complex queries with high accuracy. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Vibhor Porwal (Adobe)
- 2. Subrata Mitra (Adobe)
- 3. Fan Du (Adobe)
- 4. John Anderson (Adobe)
- 5. Nikhil Sheoran (Adobe)
- 6. Anup Rao (Adobe)
- 7. Tung Mai (Adobe)
- 8. Gautam Kowshik (Adobe)
- 9. Sapthotharan Nair (Adobe)
- 10. Sameeksha Arora (Adobe)
- 11. Saurabh Mahapatra (Adobe)
BibTeX Citation
@inproceedings{porwal_sigmod22,
title = {{Efficient Insights Discovery through Conditional Generative Model based Query Approximation}},
author = {Porwal, Vibhor and Mitra, Subrata and Du, Fan and Anderson, John and Sheoran, Nikhil and Rao, Anup and Mai, Tung and Kowshik, Gautam and Nair, Sapthotharan and Arora, Sameeksha and Mahapatra, Saurabh},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3520161},
url = {https://dl.acm.org/doi/10.1145/3514221.3520161},
year = {2022}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 8,492 | ShadowAQP: Efficient Approximate Group-by and Join Query via Attribute-oriented Sample Size Allocation and Data Generation | 2023 | VLDB | 5.4145838e-05 |
| 11,194 | Enabling Adaptive Sampling for Intra-Window Join: Simultaneously Optimizing Quantity and Quality | 2024 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 323 | DeepDB: Learn from Data, not from Queries! | 2020 | VLDB | 0.00021264788 |
| 772 | VerdictDB: Universalizing Approximate Query Processing | 2018 | SIGMOD | 0.00014147905 |
| 1,799 | DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models | 2019 | SIGMOD | 9.7326398e-05 |
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