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R2D2: Reducing Redundancy and Duplication in Data Lakes

Summary: R2D2 tackles table-level containment in data lakes with a three-stage pipeline: schema containment graph, min-max pruning, and content-level pruning—for scalable detection. It trims storage and access costs by deleting redundant datasets and reconstructing on demand under latency bounds; built on Spark (Azure Databricks/ADLS Gen2, AWS) for TB-scale lakes. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6769
Venue
SIGMOD
Year
2023
Pagerank
4.427232e-05
Overall Rank
8,910 | 38.02%
DOI
10.1145/3626762

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Rank Citing Paper Year Venue Pagerank
10,867 T-Assess: An Efficient Data Quality Assessment System Tailored for Trajectory Data 2025 VLDB 4.1945683e-05
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