<p style="text-align:justify;">
Entity resolution is a crucial step for data quality and data integration. Learning-based approaches show high effectiveness at the expense of poor efficiency. To reduce the typically high execution times, we investigate how learning-based entity resolution can be realized in a cloud infrastructure using MapReduce. We propose and evaluate two efficient MapReduce-based strategies for pair-wise similarity computation and classifier application on the Cartesian product of two input sources. Our evaluation is based on real-world datasets and shows the high efficiency and effectiveness of the proposed approaches.
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<a href="file/Kolb_CloudDB_2011.pptx" style="float:left; margin-left:20px;">
<img title="Presentation@CloudDB 2011" src="file/Kolb_CloudDB_2011.png" width="180" height="135" alt="Presentation" style="border:1px solid grey;"/>
</a>
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<h2>Keywords</h2>
<ul>
<li>MapReduce, Hadoop</li>
<li>Entity Resolution, Object matching, Similarity Join, Pair-wise comparison</li>
<li>Cartesian product</li>
<li>Machine Learning, Classification</li>
</ul>

<h2 id="bibtex_heading">BibTex</h2>
<pre id="bibtex_listing">
@inproceedings{Kolb:2011:LER:2064085.2064087,
author = {Kolb, Lars and K\\"{o}pcke, Hanna and Thor, Andreas and Rahm, Erhard},
title = {{Learning-based Entity Resolution with MapReduce}},
booktitle = {Proceedings of the third international workshop on Cloud data management},
series = {CloudDB '11},
year = {2011},
isbn = {978-1-4503-0956-1},
location = {Glasgow, Scotland, UK},
pages = {1--6},
numpages = {6},
url = {http://doi.acm.org/10.1145/2064085.2064087},
doi = {http://doi.acm.org/10.1145/2064085.2064087},
acmid = {2064087},
publisher = {ACM},
address = {New York, NY, USA},
keywords = {Cartesian product, Entity Resolution, Machine Learning, MapReduce},
}

</pre>