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
- 2. Subrata Mitra
- 3. Fan Du
- 4. John Anderson
- 5. Nikhil Sheoran
- 6. Anup Rao
- 7. Tung Mai
- 8. Gautam Kowshik
- 9. Sapthotharan Nair
- 10. Sameeksha Arora
- 11. Saurabh Mahapatra
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,621 | ShadowAQP: Efficient Approximate Group-by and Join Query via Attribute-oriented Sample Size Allocation and Data Generation | 2023 | VLDB | 4.3125802e-05 |
| 10,984 | Enabling Adaptive Sampling for Intra-Window Join: Simultaneously Optimizing Quantity and Quality | 2024 | SIGMOD | 4.1905499e-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 |
|---|---|---|---|---|
| 606 | DeepDB: Learn from Data, not from Queries! | 2020 | VLDB | 0.00019251186 |
| 1,161 | VerdictDB: Universalizing Approximate Query Processing | 2018 | SIGMOD | 0.00013579831 |
| 2,494 | DBEst: Revisiting Approximate Query Processing Engines with Machine Learning Models | 2019 | SIGMOD | 8.6457436e-05 |
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