Discovering Denial Constraints

By Xu Chu, Ihab F. Ilyas, Paolo Papotti. 2013

269 citations recorded by the source corpus.

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

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Citations in the corpus

BoostClean: Automated Error Detection and Repair for Machine Learning (2017, background): “They found that the errors in 3 of the datasets were dominated by missing cell values; 1 dataset contained functional dependency violations due to erroneous numerical attribute values, and only 1 dataset require complex user-speciﬁed denial constraints [14] to identify the errors.”
HoloClean: Holistic Data Repairs with Probabilistic Inference (2017, methodology): “Recent work has also introduced methods that automatically discover denial constraints [14].”
Profiling relational data: a survey (2015, methodology): “…SPIDER [14], ZigZag [102] Conditional inclusion dependencies [61], CINDERELLA [13], PLI [13] Foreign keys [123], [143] Denial constraints FastDC [29] Differential dependencies [128] Sequential dependencies [57] 5 Dependency detection We now survey various formalisms for detecting dependencies…”
Robust Discovery of Positive and Negative Rules in Knowledge Bases (2018, background): “, r2 in Example 1), which identify incorrect facts, similarly to denial constraints for relational data [6].”
Combining Quantitative and Logical Data Cleaning (2015, background): “(This includes constraints that use equality or inequality like denial constraints [9] and constraints that permit more approximation or difference in values than metric FDs such as differential constraints [21]).”
Trends in Cleaning Relational Data: Consistency and Deduplication (2015, methodology): “interestingness, which is a linear combination of succinctness and coverage [25].”
Data-centric Artificial Intelligence: A Survey (2023, unknown): “Initially, programmatic automation methods are used to enforce quality constraints, including integrity constraints [15], denial constraints [47], and conditional functional dependencies [27] between columns.”
Interactive and Deterministic Data Cleaning (2016, methodology): “Rule-based approaches for data cleaning are divided between methods to discover the rules from clean data [11, 12, 17, 25, 40], and algorithms and systems to apply the rules over dirty data to automatically fix the detected errors [7,13,15,18,19,21,23,24,30,32,43,46].”
RuleHub (2020, methodology): “For relational data , dependencies are discovered over the attributes of a given schema and encoded into formalisms, such as Functional Dependencies [2, 22] and Denial Constraints [12, 16].”
KATARA: A Data Cleaning System Powered by Knowledge Bases and Crowdsourcing (2015, background): “However, and as noted earlier, they can exploit the output of Katara, as rules are easier to discover from clean samples of the data [8].”
Data Cleaning: Overview and Emerging Challenges (2016, background): “Manual designing such ICs or patterns require great domain expertise, and is time-consuming, automatic discovery techniques are essential and have been proposed for various ICs [21].”
Automating Large-Scale Data Quality Verification (2018, background): “A relevant extension are denial constraints , which is a ﬁrst order logic formalism that allows the coverage of more business rules across two tuples [11].”