Camel: Managing Data for Efficient Stream Learning
Summary: Camel enables efficient stream learning with two data-management primitives: coreset selection and a historical-data buffer. Coreset selection becomes submodular maximization with a bound; buffer uses quantile sketch to compress memory and reduce forgetting. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yiming Li
- 2. Yanyan Shen
- 3. Lei Chen
Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,485 | EARLY: Efficient and Reliable Graph Neural Network for Dynamic Graphs | 2023 | SIGMOD | 5.0453531e-05 |
| 10,100 | AixelNet: A Pre-trained Model with Table-aware Adaptation for Structured Data Prediction | 2026 | SIGMOD | 4.1945683e-05 |
| 10,117 | AixelAsk: A Stepwise-Guided Retrieval and Reasoning Framework for Large Table QA | 2026 | SIGMOD | 4.1945683e-05 |
| 10,239 | BRIEF: Bi-level Coreset Selection for Efficient Instruction Tuning in LLMs | 2026 | VLDB | 4.1945683e-05 |
| 10,601 | Less is More: Efficient Time Series Dataset Condensation via Two-fold Modal Matching | 2025 | VLDB | 4.1945683e-05 |
| 10,941 | PECJ: Stream Window Join on Disorder Data Streams with Proactive Error Compensation | 2024 | SIGMOD | 4.1945683e-05 |
| 11,041 | QCore: Data-Efficient, On-Device Continual Calibration for Quantized Models | 2024 | VLDB | 4.1945683e-05 |
| 11,210 | FedCSS: Joint Client-and-Sample Selection for Hard Sample-Aware Noise-Robust Federated Learning | 2023 | SIGMOD | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 126 | Space-Efficient Online Computation of Quantile Summaries | 2001 | SIGMOD | 0.00044744986 |
| 2,886 | VISTA: Optimized System for Declarative Feature Transfer from Deep CNNs at Scale | 2020 | SIGMOD | 7.9612767e-05 |
| 2,914 | DDSketch: A Fast and Fully-Mergeable Quantile Sketch with Relative-Error Guarantees | 2019 | VLDB | 7.9118579e-05 |
| 3,319 | Sketching Linear Classifiers over Data Streams | 2018 | SIGMOD | 7.226439e-05 |
| 3,808 | SketchML: Accelerating Distributed Machine Learning with Data Sketches | 2018 | SIGMOD | 6.7455428e-05 |
| 4,439 | TencentRec: Real-time Stream Recommendation in Practice | 2015 | SIGMOD | 6.1885354e-05 |
| 5,806 | BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees | 2019 | SIGMOD | 5.3200643e-05 |
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Semantically Similar Papers
| Overall Rank | Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,446 | Stable Learned Bloom Filters for Data Streams | 2020 | VLDB | 6.1800659e-05 |
| 1,045 | Adaptive Stream Resource Management Using Kalman Filters | 2004 | SIGMOD | 0.00014472777 |
| 1,737 | QuickSel: Quick Selectivity Learning with Mixture Models | 2020 | SIGMOD | 0.00010720294 |
| 8,281 | Optimizing Data Acquisition to Enhance Machine Learning Performance | 2024 | VLDB | 4.5435639e-05 |
| 2,955 | Space- and Time-Efficient Deterministic Algorithms for Biased Quantiles over Data Streams | 2006 | PODS | 7.8239173e-05 |
| 10,840 | Learned Cost Models for Query Optimization: From Batch to Streaming Systems | 2025 | VLDB | 4.1945683e-05 |
| 8,009 | CAMAL: Optimizing LSM-trees via Active Learning | 2024 | SIGMOD | 4.6066863e-05 |
| 4,076 | Quantiles over Data Streams: An Experimental Study | 2013 | SIGMOD | 6.4680854e-05 |
| 3,319 | Sketching Linear Classifiers over Data Streams | 2018 | SIGMOD | 7.226439e-05 |
| 9,915 | Camel: Efficient Compression of Floating-Point Time Series | 2024 | SIGMOD | 4.2561557e-05 |