Declarative Data Cleaning: Language, Model, and Algorithms

By H. Galhardas, D. Florescu, Dennis E. Shasha, E. Simon, Cristian-Augustin Saita. 2001

417 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Interactive and Deterministic Data Cleaning (2016, background): “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].”
Automating Large-Scale Data Quality Verification (2018, methodology): “Galhardas et al. propose a declarative language AJAX for data cleaning as an extension of SQL as well as an execution model for executing data cleaning programs [14].”
Hippocratic Databases (2002, methodology): “Data Preprocessing The Data Accuracy Analyzer may run some data cleansing functions [19] [41] against the data to check for accuracy either before or after data insertion, thus addressing the Principle of Accuracy.”
Data Integration for the Relational Web (2009, background): “There are other cleaning systems with operators that could be useful to Octopus [12].”
TAILOR: a record linkage toolbox (2002, background): “[12] propose a declarative language for the logical specification of data cleaning operations, along with a framework for specifying various data cleaning techniques at the logical and physical level; record linkage is one of these techniques.”
Management of probabilistic data: foundations and challenges (2007, background): “This is a central problem in data cleaning and data integration, and has also been called record linkage, deduplication, or merge-purge [34, 3, 13, 37, 43, 49, 15, 80, 7].”
Interactive deduplication using active learning (2002, background): “Recently, there has been renewed interest in the database community on the data cleaning problem [26, 10 , 25] comprising several aspects, including, data segmentation, deduplication, outlier detection, standardization and schema mapping.”
Large-Scale Deduplication with Constraints Using Dedupalog (2009, methodology): “AJAX captures operations that we do not consider such as information extraction and approximate string join.”
Combining Quantitative and Logical Data Cleaning (2015, background): “In logical data cleaning, error detection and repair is typically performed using declarative cleaning programs [12].”
Data integration: the teenage years (2006, background): “[47] Helena Galhardas, Daniela Florescu, Dennis Shasha, Eric Simon, and Cristian-Augustin Saita.”
Declarative Support for Sensor Data Cleaning (2006, methodology): “Extensions to this paradigm include the AJAX tool [21], an extensible, declarative means of specifying cleaning operations in a data warehouse.”
Discovering data quality rules (2008, background): “Many data cleaning tools support the discovery of statistical trends and potential join paths [10], schema restructuring and object identiflcation [21, 12 ], but to the best of our knowledge none of these tools focus on the discovery of contextual dependency rules.”