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)
Incoming Non-self Citations Over Time
Authors
- 1. Raunak Shah
- 2. Koyel Mukherjee
- 3. Atharv Tyagi
- 4. Sai Keerthana Karnam
- 5. Dhruv Joshi
- 6. Shivam Bhosale
- 7. Subrata Mitra
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,871 | T-Assess: An Efficient Data Quality Assessment System Tailored for Trajectory Data | 2025 | VLDB | 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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