Accuracy
is a most important data quality dimension and its assessment is a key issue in data management. Most of current studies focus on how to qualitatively analyze accuracy
dimension and the analysis depends heavily on experts’ knowledge. Seldom work is given on how to automatically
quantify accuracy
dimension. Based on Jensen–Shannon divergence (JSD) measure, we propose accuracy
of data can be automatically quantified by comparing data with its entity’s most approximation
in available context
. To quickly identify most approximation
in large scale data sources, locality-sensitive hashing (LSH
) is employed to extract most approximation
at multiple levels, namely column, record and field level. Our approach can not only give each data source an objective accuracy score very quickly as long as context
member is available but also avoid human’s laborious interaction. As an automatic accuracy
assessment solution in multiple-source environment, our approach is distinguished, especially for large scale data sources. Theory and experiment show our approach performs well in achieving metadata on accuracy dimension.
Published by Expert Systems With Applications (ELSEVIER)The online version is available at http://dx.doi.org/10.1016/j.eswa.2009.08.023
.
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