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QOCO: A Query Oriented Data Cleaning System with Oracles

Summary: QOCO uses user-defined query-driven materialized views as triggers to surface still-missing or erroneous data. It crowdsources domain expertise (oracles) to diagnose/root-cause fixes, with pruning to minimize questions, demonstrated on a World Cup games database. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
11077
Venue
VLDB
Year
2015
Pagerank
4.3749064e-05
Overall Rank
9,196 | 36.03%
DOI
-

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
7,605 The Computation of Optimal Subset Repairs 2020 VLDB 4.697534e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
263 CrowdER: Crowdsourcing Entity Resolution 2012 VLDB 0.00029862413
487 Why Not? 2009 SIGMOD 0.00022050218
809 Curated Databases 2008 PODS 0.00016430384
2,184 A Sample-and-Clean Framework for Fast and Accurate Query Processing on Dirty Data 2014 SIGMOD 9.3429789e-05
2,334 Counting with the Crowd 2013 VLDB 9.0161817e-05
2,797 Query-Oriented Data Cleaning with Oracles 2015 SIGMOD 8.1108589e-05
4,416 CrowdMatcher: Crowd-Assisted Schema Matching 2014 SIGMOD 6.2039225e-05
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