Improving Data Quality: Consistency and Accuracy

By Gao Cong, Wenfei Fan, Floris Geerts, Xibei Jia, Shuai Ma. Very Large Data Bases Conference, 2007

403 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/52032bb1547b859bc4e74f671a74869173c7b1c4

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Citations in the corpus

A Data Quality Metric (DQM): How to Estimate the Number of Undetected Errors in Data Sets (2016, background): “th [27, 7, 12, 28, 13, 20]; previous works deﬁne data quality through counting the losses to gold standard data or violations of the constraint rules set forth by domainspeciﬁc heuristics and experts [35, 5, 8, 11, 23]. In practice, however, such ground truth data or rules are not readily available ”
Holistic data cleaning: Putting violations into context (2013, background): “Most of the previous approaches [6], [8] were dealing only with equality, and can be seen as special cases of our work.”
Large-Scale Deduplication with Constraints Using Dedupalog (2009, background): “A related line of work considers the problem of automatically repairing a database instance so as to satisfy all hard constraints [52], [53].”
Profiling relational data: a survey (2015, methodology): “From a data cleaning standpoint, there are solutions for incremental detection of Fd and Cfd violations [50], and incremental data repairing with respect to Fd s and Cfd s [30].”
Trends in Cleaning Relational Data: Consistency and Deduplication (2015, methodology): “Similarly, violations of CFD constraints have been addressed in multiple proposals, either automatically [31] or by involving humans in the loop [126].”
NADEEF: a commodity data cleaning system (2013, methodology): “For the previous case or when the number of rules (e.g., CFDs) is small, several algorithms have been proposed to check their consistency [12].”
Don't be SCAREd: use SCalable Automatic REpairing with maximal likelihood and bounded changes (2013, methodology): “Most existing solutions to repair dirty databases by value modification follow constraint-based repairing approaches [3, 19, 18], which search for minimal change of the database to satisfy a predefined set of constraints.”
Discovering data quality rules (2008, methodology): “from the original database D. Cong et al.[ 9 ] propose repair algorithms to repair D based on the CFDs and potential updates to D. We focus on discovering CFDs and identifying tuples that violate these rules.”
Data X-Ray: A Diagnostic Tool for Data Errors (2015, background): “Again, there is a large arsenal of tools in this category that use rules [7, 14] and functional dependencies [12, 26], while there is also work that focuses on generating repairs in an interactive fashion, using user”
Open Civic Data: Of the People, For the People, By the People (2014, background): “Practical and theoretical work on data provenance [30, 31, 32] and data quality [33] are also extremely relevant.”
Data Cleaning: Overview and Emerging Challenges (2016, methodology): “Other techniques FDsvaluemodiﬁcation[13] FDshypergraph[46] CFDsvaluemodiﬁcation[25] Holisticdatacleaning[22] LLUNATIC[33] Recordmatchinganddatarepairing[31] NADEEF[26] Generateoptimaltablaux[36] Uniﬁedrepair[19] Relativetrust[11] Continuousdatacleaning[73] Potter’sWheel[64] GDR[82] KATARA[23]…”
KATARA: A Data Cleaning System Powered by Knowledge Bases and Crowdsourcing (2015, background): “Several attempts have been made to do repairing based on integrity constraints (ICs) [1, 9, 11, 17, 20]; they try to find a consistent database that satisfies given ICs in a minimum cost.”
HoloClean: Holistic Data Repairs with Probabilistic Inference (2017, background): “There has been a significant amount of work on methods for data cleaning, including approaches that rely on integrity constraints [15, 17, 22, 26], methods that leverage external information, such as data obtained by experts [25, 47] or data in existing knowledge bases or dictionaries [16, 25, 32, 4”
Conditional functional dependencies for capturing data inconsistencies (2008, methodology): “While there has been recent preliminary work on this issue based on cfds alone [Cong et al. 2007], it deserves further investigation in the presence of both cfds and cinds.”
Guided data repair (2011, background): “Most of the existing data repair approaches (e.g., [2, 6, 7, 16]) focus on providing fully automated solutions using diﬀerent heuristics to select updates that would introduce minimal changes to the data, which could be risky especially for critical data.”
On approximating optimum repairs for functional dependency violations (2009, methodology): “[13]? What can we say about finding (approximate) optimum repairs w.”