<p style="text-align:justify;">
Entity matching is an important and difficult step for integrating web data. To reduce the typically high execution time for matching we investigate how we can perform entity matching in parallel on a distributed infrastructure. We propose different strategies to partition the input data and generate multiple match tasks that can be independently executed. One of our strategies supports both, blocking to reduce the search space for matching and parallel matching to improve efficiency. Special attention is given to the number and size of data partitions as they impact the overall communication overhead and memory requirements of individual match tasks. We have developed a service-based distributed infrastructure for the parallel execution of match workflows. We evaluate our approach in detail for different match strategies for matching real-world product data of different web shops. We also consider caching of input entities and affinity-based scheduling of match tasks.
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<a href="file/Kolb_QDB_2010.pptx" style="float:left; margin-left:20px;">
<img title="Presentation@QDB 2010" src="file/Kolb_QDB_2010.png" width="180" height="135" alt="Presentation" style="border:1px solid grey;"/>
</a>
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<h2>Keywords</h2>
<ul>
<li>Distributed computing, Parallelism</li>
<li>Entity Resolution, Object matching, Similarity Join, Pair-wise comparison</li>
<li>Blocking, Clustering</li>
</ul>

<h2 id="bibtex_heading">BibTex</h2>
<pre id="bibtex_listing">
@inproceedings{data_partitioning_for_parallel_entity_matching,
author = {Toralf Kirsten and
Lars Kolb and
Michael Hartung and
Anika Gross and
Hanna K{\\"o}pcke and
Erhard Rahm},
title = {{Data Partitioning for Parallel Entity Matching}},
booktitle = {8th International Workshop on Quality in Databases},
year = {2010}
}
</pre>