Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.eswa.2009.08.023
Title: Automatic accuracy assessment via hashing in multiple-source environment
Authors: Han, J.
Jiang, D. 
Li, L.
Keywords: Accuracy
Automatic assessment
Context
Data quality
Jensen-Shannon divergence (JSD)
Locality-sensitive hashing (LSH)
Issue Date: 2010
Source: Han, J., Jiang, D., Li, L. (2010). Automatic accuracy assessment via hashing in multiple-source environment. Expert Systems with Applications 37 (3) : 2609-2620. ScholarBank@NUS Repository. https://doi.org/10.1016/j.eswa.2009.08.023
Abstract: 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. © 2009 Elsevier Ltd. All rights reserved.
Source Title: Expert Systems with Applications
URI: http://scholarbank.nus.edu.sg/handle/10635/39015
ISSN: 09574174
DOI: 10.1016/j.eswa.2009.08.023
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

2
checked on Dec 7, 2017

WEB OF SCIENCETM
Citations

1
checked on Nov 23, 2017

Page view(s)

66
checked on Dec 18, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.