Efficient Construction of Approximate Ad-Hoc ML models Through Materialization and Reuse
Summary: Proposes materialization and reuse of previously built ML models to answer new analytic queries over OLAP-aligned data. A cost-based framework selects and composes models (GLMs, K-Means, GMM) to approximate new queries, yielding large speedups on big data. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Sona Hasani
- 2. Saravanan Thirumuruganathan
- 3. Abolfazl Asudeh
- 4. Nick Koudas
- 5. Gautam Das
Incoming Citations (Sorted by Pagerank)
Showing 6 of 6 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,423 | PrIU: A Provenance-Based Approach for Incrementally Updating Regression Models | 2020 | SIGMOD | 6.1925724e-05 |
| 5,062 | Optimizing Machine Learning Inference Queries with Correlative Proxy Models | 2022 | VLDB | 5.7172262e-05 |
| 6,464 | Materialization and Reuse Optimizations for Production Data Science Pipelines | 2022 | SIGMOD | 5.0471003e-05 |
| 8,651 | ApproxML: Efficient Approximate Ad-Hoc ML Models Through Materialization and Reuse | 2019 | VLDB | 4.4710004e-05 |
| 10,609 | Less is More: Efficient Time Series Dataset Condensation via Two-fold Modal Matching | 2025 | VLDB | 4.1905499e-05 |
| 11,487 | Shahin: Faster Algorithms for Generating Explanations for Multiple Predictions | 2021 | SIGMOD | 4.1905499e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 15 of 15 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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