Cloud infrastructures enable the efficient parallel execution of data-intensive tasks such as entity resolution on large datasets. We investigate challenges and possible solutions of using the MapReduce programming model for parallel entity resolution. In particular, we propose and evaluate two MapReduce-based implementations for Sorted Neighborhood blocking that either use multiple MapReduce jobs or apply a tailored data replication.

<a href="file/Kolb_BTW_2011.pptx" style="float:left; margin-left:20px;">
<img title="Presentation@BTW 2011" src="file/Kolb_BTW_2011.png" width="180" height="135" alt="Presentation" style="border:1px solid grey;"/>
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<h2>Keywords</h2>
<ul>
<li>MapReduce, Hadoop</li>
<li>Entity Resolution, Object matching, Similarity Join, Pair-wise comparison</li>
<li>Blocking, Sliding Window, Sorted Neighborhood</li>
</ul>

<h2 id="bibtex_heading">BibTex</h2>
<pre id="bibtex_listing">
@inproceedings{DBLP:conf/btw/KolbTR09,
author = {Lars Kolb and
Andreas Thor and
Erhard Rahm},
title = {{Parallel Sorted Neighborhood Blocking with MapReduce}},
booktitle = {BTW},
year = {2011},
pages = {45-64},
crossref = {DBLP:conf/btw/2011},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
</pre>