Approximation Algorithms for Large Scale Data Analysis
Summary: Use approximation algorithms to recover faster polynomial-time and lower-query solutions for large-scale data tasks, circumventing fine-grained conditional lower bounds (e.g., SETH). Highlights tradeoffs among approximation quality, running time, and side-information/query complexity. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Barna Saha
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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 |
|---|---|---|---|---|
| 643 | Corleone: Hands-Off Crowdsourcing for Entity Matching | 2014 | SIGMOD | 0.00018754451 |
| 866 | Leveraging Transitive Relations for Crowdsourced Joins | 2013 | SIGMOD | 0.00015801196 |
| 1,841 | Crowdsourcing Algorithms for Entity Resolution | 2014 | VLDB | 0.00010348858 |
| 4,104 | Online Entity Resolution Using an Oracle | 2016 | VLDB | 6.4493809e-05 |
| 9,684 | How to Design Robust Algorithms using Noisy Comparison Oracle | 2021 | VLDB | 4.3047774e-05 |
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