Conditional Functional Dependencies for Data Cleaning

By Philip Bohannon, Wenfei Fan, Floris Geerts, Xibei Jia, Anastasios Kementsietsidis. IEEE International Conference on Data Engineering, 2007

440 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/74feb1f64b3773173e296c93d1e72b9734914c37

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Citations in the corpus

Data Cleaning: Overview and Emerging Challenges (2016, background): “…has been a surge of interest from both industry and academia on different aspects of data cleaning including new abstractions [10, 30, 77, 22, 33, 14, 73], interfaces [1, 26], approaches for scalability [75, 45, 3, 53, 67], and crowdsourcing techniques [35, 69, 62, 18, 82, 23, 37, 60, 61, 76,…”
Automating Large-Scale Data Quality Verification (2018, background): “A popular paradigm for deﬁning dependencies between columns are functional dependencies and conditional functional dependencies [6]; there exist fast, approximate algorithms for discovering them [31].”
Data-centric Artificial Intelligence: A Survey (2023, positive): “Initially, programmatic automation methods are used to enforce quality constraints, including integrity constraints [15], denial constraints [47], and conditional functional dependencies [27] between columns.”
Combining Quantitative and Logical Data Cleaning (2015, methodology): “While FDs and extensions including conditional FDs [6] and matching dependencies [11] have been used extensively for data cleaning, ours is the first application of metric FDs to cleaning.”
Trends in Cleaning Relational Data: Consistency and Deduplication (2015, background): “Conditional functional dependencies (CFDs), an extension of FDs, are capable of capturing FDs that hold partially on the data [18].”
HoloClean: Holistic Data Repairs with Probabilistic Inference (2017, methodology): “First, we focus on data repairing methods that rely on integrity constraints [7, 11, 15].”
Large-Scale Deduplication with Constraints Using Dedupalog (2009, neutral): “Finally, we conclude with the important physical optimizations (execution strategies) that allow our algorithms to scale to large datasets.”
On approximating optimum repairs for functional dependency violations (2009, background): “An extension to traditional functional dependencies called conditional functional dependencies (CFDs) has recently been introduced [9, 13] that is capable of representing accurate information and is useful in data cleaning.”
Discovering data quality rules (2008, methodology): “This is equivalent to applying the decomposition rule [5, 19].”
Improving Data Quality: Consistency and Accuracy (2007, methodology): “A variety of constraint formalisms have been proposed [6, 4, 8, 26, 27].”
Conditional functional dependencies for capturing data inconsistencies (2008, methodology): “RELATED WORK The authors introduced CFDs in earlier work [Bohannon et al. 2007].”
Discovering Denial Constraints (2013, methodology): “dependencies (FDs), and their extension conditional functional dependencies (CFDs) have been proposed for data quality management [7].”