LPStream: Fine-grained Lazy Provenance for Stream Processing
Summary: LPStream introduces fine-grained lazy provenance for stream processing, avoiding runtime provenance overhead. When needed, it replays from a suitable checkpoint to generate provenance, outperforming eager provenance and no-provenance baselines. (summarized by gpt-5-nano on Feb 09 2026)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 600 | Linear Road: A Stream Data Management Benchmark | 2004 | VLDB | 0.0001938744 |
| 611 | Lineage Tracing for General Data Warehouse Transformations | 2001 | VLDB | 0.00019231115 |
| 923 | Provenance and Scientific Workflows: Challenges and Opportunities | 2008 | SIGMOD | 0.0001527609 |
| 1,824 | DBNotes: A Post-It System for Relational Databases based on Provenance | 2005 | SIGMOD | 0.00010405194 |
| 2,027 | Titian: Data Provenance Support in Spark | 2016 | VLDB | 9.7437067e-05 |
| 2,280 | SMOKE: Fine-grained Lineage at Interactive Speed | 2018 | VLDB | 9.1111033e-05 |
| 4,488 | Analyzing Efficient Stream Processing on Modern Hardware | 2019 | VLDB | 6.145117e-05 |
| 7,710 | Ananke: A Streaming Framework for Live Forward Provenance | 2021 | VLDB | 4.6719822e-05 |
| 8,140 | Erebus: Explaining the Outputs of Data Streaming Queries | 2023 | VLDB | 4.5768015e-05 |
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