AJAX: an extensible data cleaning tool

By H. Galhardas, D. Florescu, Dennis E. Shasha, E. Simon. 2000

232 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

Benchmark and Survey of Automated Machine Learning Frameworks (2019, background): “For over two decades semi-automatic, interactive systems existed to aid a data scientist in data cleaning (Galhardas et al., 2000; Raman & Hellerstein, 2001).”
Potter's Wheel: An Interactive Data Cleaning System (2001, background): “[3]), and algorithms for finding approximate duplicates in data [13, 17, 10].”
Conceptual modeling for ETL processes (2002, background): “Finally, apart from the commercial ETL tools [1, 7, 9, 21, 25] there also exist research efforts including [3, 10, 20, 23, 26, 27, 34, 35, 36].”
Data X-Ray: A Diagnostic Tool for Data Errors (2015, background): “Data management research has supplied a variety of tools to deal with errors originating from integrating data from multiple sources [1,5,47,49], identifying data items that refer to the same entity [33,37], resolving conflicts [22, 58, 59], and providing language extensions for cleaning [28].”
Wrangler: interactive visual specification of data transformation scripts (2011, background): “Both prior work [8, 17, 22] and empirical data guided the language design.”
Guided data repair (2011, methodology): “For example, AJAX [12] proposes a declarative language to eliminate duplicates during data transformations.”
Data Cleaning: Problems and Current Approaches. (2000, background): “Also, the discovery of data cleaning rules during warehouse design can suggest improvements to the constraints enforced by existing schemas.”
Processing (2020, unknown): “A major part of the research related to data warehousing deals with managing proprietary warehouses [8] [9] [10] [11].”
Towards reliable interactive data cleaning: a user survey and recommendations (2016, methodology): “, providing de-duplication examples, descriptions of outliers) that also enables physical implementation choices to be guided by either automated tools or the technical experts that are tasked with implementing data cleaning at scale [16,19].”
Improving Data Quality: Consistency and Accuracy (2007, background): “There has been a host of work on the merge-purge problem ( e.g., [15, 21, 28]) for the elimination of approximate duplicates .”
Frameworks for entity matching: A comparison (2010, methodology): “We focus on research prototypes but do not consider the more general system approaches on data cleaning and data integration, such as AJAX [27] and Potters’s Wheel [48].”
A cost-based model and effective heuristic for repairing constraints by value modification (2005, methodology): “A related topic of interest in data cleaning is the elimination of approximate duplicates, also referred to as the object identity or merge/purge problem; see, for example, [8, 11, 15, 20, 21, 28].”
Adaptive Name Matching in Information Integration (2003, methodology): “On one end of this spectrum lie rule-based methods based on equational theory [10, 8, 9, 14] that require a human expert to specify the conditions for records to be equivalent in a declarative rule language.”
Learning to match and cluster large high-dimensional data sets for data integration (2002, methodology): “Previous work in this area includes work in distance functions for matching [14, 3, 9, 8] and scalable matching [2] and clustering [13] algorithms.”