Potter's Wheel: An Interactive Data Cleaning System

By Vijayshankar Raman, Joseph M Hellerstein. 2001

657 citations recorded by the source corpus.

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In Software Engineering

Citations in the corpus

Benchmark and Survey of Automated Machine Learning Frameworks (2019, background): “Using different representations of expert knowledge, like integrity constraints or first order logic, low quality data can be detected and corrected automatically (Raman & Hellerstein, 2001; Hellerstein, 2008; Chu et al., 2015, 2016).”
AlphaClean: Automatic Generation of Data Cleaning Pipelines (2019, methodology): “Languages for data transformations have been well-studied, and include seminal works by Raman and Hellerstein [39] for schema transformations and Galhardas et al.”
Survey on Automated Machine Learning (2019, background): “Using different representations of expert knowledge, like integrity constraints or first order logic, low quality data can be automatically detected and corrected (Raman and Hellerstein, 2001; Hellerstein, 2008; Chu et al., 2015, 2016).”
Large-Scale Deduplication with Constraints Using Dedupalog (2009, methodology): “Closely related to declarative data cleaning systems are the interactive data cleaning systems such as Potter’s wheel [47] and D-Dupe [19].”
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.”
Duplicate Record Detection: A Survey (2007, background): “Multiple parsing methods have been proposed recently in the literature (e.g., [21], [22], [23], [24], [25]) and the area continues to be an active field of research.”
A Comparison of String Distance Metrics for Name-Matching Tasks (2003, background): “(Hernandez & Stolfo 1995; Galhardas et al. 2000; Raman & Hellerstein 2001).”
Interactive and Deterministic Data Cleaning (2016, background): “Interactive systems for data transformation [27,37,44] also reason about the updated attribute to learn transformation rules.”
Improving Data Quality: Consistency and Accuracy (2007, background): “Potter’s Wheel [30] is an interactive data cleaning system, which supports a sliding-window interface, and combines data transformations and error detection (syntax and irregularities).”
An interactive clustering-based approach to integrating source query interfaces on the deep Web (2004, methodology): “To determine if a field is composite, we adopt a simplified structure extraction process as employed in [26] for data cleaning.”
Data X-Ray: A Diagnostic Tool for Data Errors (2015, background): “Data management research has long recognized the importance of data quality, and has developed an extensive arsenal of data cleaning approaches based on rules, statistics, analyses, and interactive tools [1,25,35,50,51].”
Trends in Cleaning Relational Data: Consistency and Deduplication (2015, methodology): “Potter’s Wheel Potter’s Wheel [94] is an interactive data cleaning system that tightly integrates data transformation and discrepancy detection.”
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.”
How Data Science Workers Work with Data: Discovery, Capture, Curation, Design, Creation (2019, methodology): “Raman and Hellerstein invoked craft as a guiding metaphor in Potter’s Wheel, a tool for cleaning data [77].”
Data Curation at Scale: The Data Tamer System (2013, methodology): “This expert uses an approach called minimum description length (MDL) that uses a measure similar to Jaccard similarity to compare two attributes [17].”