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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)

Paper ID
1830
Venue
PODS
Year
2021
Pagerank
4.1945683e-05
Overall Rank
11,443 | 20.40%
DOI
10.1145/3452021.3458813

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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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