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 (University of California Berkeley)
BibTeX Citation
@inproceedings{saha_pods21,
address = {New York, NY, USA},
series = {{PODS} '21},
title = {{Approximation Algorithms for Large Scale Data Analysis}},
url = {https://dl.acm.org/doi/10.1145/3452021.3458813},
doi = {10.1145/3452021.3458813},
booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
publisher = {Association for Computing Machinery},
author = {Saha, Barna},
year = {2021}
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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 |
|---|---|---|---|---|
| 439 | Corleone: Hands-Off Crowdsourcing for Entity Matching | 2014 | SIGMOD | 0.00018464913 |
| 852 | Leveraging Transitive Relations for Crowdsourced Joins | 2013 | SIGMOD | 0.00013604253 |
| 1,443 | Crowdsourcing Algorithms for Entity Resolution | 2014 | VLDB | 0.00010773106 |
| 3,602 | Online Entity Resolution Using an Oracle | 2016 | VLDB | 7.2691521e-05 |
| 9,820 | How to Design Robust Algorithms using Noisy Comparison Oracle | 2021 | VLDB | 5.214913e-05 |
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