Column heterogeneity as a measure of data quality
Data quality is a serious concern in every data management application, and a variety of quality measures have been proposed, including accuracy, freshness and completeness, to capture the common sources of data quality degradation. We identify and focus attention on a novel measure, column heteroge...
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| Main Authors: | , , , , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2007
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/4165 https://ink.library.smu.edu.sg/context/sis_research/article/5168/viewcontent/Dai2006ColumnHeterogeneityasa.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | Data quality is a serious concern in every data management application, and a variety of quality measures have been proposed, including accuracy, freshness and completeness, to capture the common sources of data quality degradation. We identify and focus attention on a novel measure, column heterogeneity, that seeks to quantify the data quality problems that can arise when merging data from different sources. We identify desiderata that a column heterogeneity measure should intuitively satisfy, and discuss a promising direction of research to quantify database column heterogeneity based on using a novel combination of cluster entropy and soft clustering. Finally, we present a few preliminary experimental results, using diverse data sets of semantically different types, to demonstrate that this approach appears to provide a robust mechanism for identifying and quantifying database column heterogeneity. |
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