Accelerating Aggregation Queries on Unstructured Streams of Data
Summary: InQuest: streaming, multimodal aggregation over unstructured data using cheap proxy models plus sampling to limit expensive oracle invocations, producing real-time approximate query answers with statistical guarantees. Theory: expected error on stationary streams decays ∝1/(oracle budget); evaluation: matches streaming baselines with up to 5× fewer oracle calls and improves RMSE vs a state-of-the-art batch method. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Matthew Russo
- 2. Tatsunori Hashimoto
- 3. Daniel Kang
- 4. Yi Sun
- 5. Matei Zaharia
Incoming Citations (Sorted by Pagerank)
Showing 9 of 9 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,149 | Abacus: A Cost-Based Optimizer for Semantic Operator Systems | 2026 | VLDB | 5.655398e-05 |
| 9,238 | PilotDB: Database-Agnostic Online Approximate Query Processing with A Priori Error Guarantees | 2025 | SIGMOD | 4.3648789e-05 |
| 9,989 | Deep Research is the New Analytics System: Towards Building the Runtime for AI-Driven Analytics | 2026 | CIDR | 4.1905499e-05 |
| 10,064 | Cut Costs, Not Accuracy: LLM-Powered Data Processing with Guarantees | 2026 | SIGMOD | 4.1905499e-05 |
| 10,215 | Task Cascades for Efficient Unstructured Data Processing | 2026 | SIGMOD | 4.1905499e-05 |
| 10,349 | Efficient Approximate Query Processing with Block Sampling | 2025 | CIDR | 4.1905499e-05 |
| 10,394 | MAST: Towards Efficient Analytical Query Processing on Point Cloud Data | 2025 | SIGMOD | 4.1905499e-05 |
| 10,532 | Scalable Complex Event Processing on Video Streams | 2025 | SIGMOD | 4.1905499e-05 |
| 10,675 | Déjà Vu: Efficient Video-Language Query Engine with Learning-based Inter-Frame Computation Reuse | 2025 | VLDB | 4.1905499e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 13 of 13 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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Semantically Similar Papers
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| 5,062 | Optimizing Machine Learning Inference Queries with Correlative Proxy Models | 2022 | VLDB | 5.7172262e-05 |
| 5,799 | Learned Approximate Query Processing: Make it Light, Accurate and Fast | 2021 | CIDR | 5.3219666e-05 |
| 1,065 | Processing Complex Aggregate Queries over Data Streams | 2002 | SIGMOD | 0.00014344675 |
| 10,758 | QUEST: Query Optimization in Unstructured Document Analysis | 2025 | VLDB | 4.1905499e-05 |
| 9,162 | Estimating Quantiles from the Union of Historical and Streaming Data | 2017 | VLDB | 4.380727e-05 |
| 11,655 | Query-Driven Learning for Next Generation Predictive Modeling & Analytics | 2019 | SIGMOD | 4.1905499e-05 |
| 4,703 | Accelerating Approximate Aggregation Queries with Expensive Predicates | 2021 | VLDB | 5.9793615e-05 |
| 9,311 | On Efficient Approximate Queries over Machine Learning Models | 2023 | VLDB | 4.3535588e-05 |
| 11,429 | Accelerating Queries over Unstructured Data with ML | 2021 | CIDR | 4.1905499e-05 |