The system offers the possibility
of performing several algebraic operations over an underlying dataset. Optimization
algorithms are also provided for the CPU usage for certain classes
of operators. The general idea behind Potter??™s Wheel is that users build data
transformations in iterative and interactive ways. In the background, Potter??™s
Wheel automatically infers structures for data values in terms of user-defined
domains, and accordingly checks for constraint violations. Users gradually
build transformations to clean the data by adding or undoing transforms on a
spreadsheet-like interface; the effect of a transform is shown at once on records
visible on screen. These transforms are specified either through simple graphical
operations, or by showing the desired effects on example data values.
??? Data.quality.and.cleaning: Jarke, List, and Koller (2000) present an extensive
review of data quality problems and related literature, along with quality
management methodologies. Rundensteiner (1999) offers a discussion on
various aspects on data transformations. Sarawagi (2000) presents a similar
collection of papers in the field of data cleaning including a survey (Rahm &
Hai Do, 2000) that provides an extensive overview of the field, along with research
issues and a review of some commercial tools and solutions on specific
problems (Borkar, Deshmuk, & Sarawagi, 2000; Monge, 2000).
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