Trends in Cleaning Relational Data: Consistency and Deduplication

By Ihab F. Ilyas, Xu Chu. 2015

168 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems and Management, Management Science and Operations Research

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Citations in the corpus

AlphaClean: Automatic Generation of Data Cleaning Pipelines (2019, background): “This subsumes popular quality measures such as integrity constraint violations [25] and numerical outlier detection [6], and can readily express application-specific quality measures such as machine learning training accuracy or goodness-of-fit to a model.”
HoloClean: Holistic Data Repairs with Probabilistic Inference (2017, background): “Significant efforts have been made to automate both tasks, and several surveys summarize these results [23, 31, 42].”
Data Cleaning: Overview and Emerging Challenges (2016, background): “Most of the materials in the ﬁrst part of the tutorial come from our survey in Foundations and Trends in Databases [41].”
Automating Large-Scale Data Quality Verification (2018, background): “Ilyas et al. provide an overview on standard consistency definitions [23].”
The Eﬀects of Data Quality on Machine Learning Performance (2022, background): “Furthermore, there is a plethora of existing research on how to detect duplicates and how to remove them [13, 35].”
MacroBase: Prioritizing Attention in Fast Data (2016, methodology): “The literature contains several recent explanation techniques leveraging decision-tree [23] and Apriori-like [39, 80] pruning, grid search [68, 82], data cubing [69], Bayesian statistics [78], visualization [17,62], causal reasoning [81], and several others [31, 43, 51, 58].”
MacroBase: Analytic Monitoring for the Internet of Things (2016, unknown): “These techniques—and related data cleaning techniques [24,31]—are powerful and can handle non-”
Data Integration: The Current Status and the Way Forward (2018, background): “…Records are transformed into common units (say Euros to Dollars) via a series of transformation rules, modules and services; (c) Errors are cleaned [8]; typically 15% of the values are missing or incorrect, and most integration tools expect these errors to be at least spotted or nulls to be…”
The effects of data quality on machine learning performance on tabular data (2022, unknown): “Furthermore, there is a plethora of existing research on how to detect duplicates and how to remove them [14, 34].”