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    Explicitly Involving the User in a Data Cleaning Process

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    Data cleaning and Extract-Transform-Load processes are usually mod- eled as graphs of data transformations. These graphs typically involve a large number of data transformations, and must handle large amounts of data. The involvement of the users responsible for executing the corre- sponding programs over real data is important to tune data transforma- tions and to manually correct data items that cannot be treated automat- ically. In this paper, we extend the notion of data cleaning graph in order to better support the user involvement in data cleaning processes. We propose that data cleaning graphs include: (i) data quality constraints to help users to identify the points of the graph and the records that need their attention and (ii) manual data repairs for representing the way users can provide the feedback required to manually clean some data items. We provide preliminary experimental results that show, for a real-world data cleaning process, the significant gains obtained with our approach in terms of the quality of the data produced and the cost incurred by users in data visualization and updating tasks.

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    Description

    Title : Explicitly Involving the User in a Data Cleaning Process
    Author(s) : Helena Galhardas, Antónia Lopes, Emanuel Santos
    Abstract : Data cleaning and Extract-Transform-Load processes are usually mod- eled as graphs of data transformations. These graphs typically involve a large number of data transformations, and must handle large amounts of data. The involvement of the users responsible for executing the corre- sponding programs over real data is important to tune data transforma- tions and to manually correct data items that cannot be treated automat- ically. In this paper, we extend the notion of data cleaning graph in order to better support the user involvement in data cleaning processes. We propose that data cleaning graphs include: (i) data quality constraints to help users to identify the points of the graph and the records that need their attention and (ii) manual data repairs for representing the way users can provide the feedback required to manually clean some data items. We provide preliminary experimental results that show, for a real-world data cleaning process, the significant gains obtained with our approach in terms of the quality of the data produced and the cost incurred by users in data visualization and updating tasks.
    Subject : unspecified
    Area : Other
    Language : English
    Year : 2011

    Affiliations University of Lisbon
    Journal : Quality
    Pages : 28
    Url : http://docs.di.fc.ul.pt/jspui/bitstream/10455/6674/3/TR-2010-03.pdf

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