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The effectiveness and scalability of MapReduce-based implementations of complex data-intensive tasks depend on an even redistribution of data between map and reduce tasks. In the presence of skewed data, sophisticated redistribution approaches thus become necessary to achieve load balancing among all reduce tasks to be executed in parallel. For the complex problem of entity resolution, we propose and evaluate two approaches for such skew handling and load balancing.

Entity resolution is a crucial step for data quality and data
integration. Learning-based approaches show high effective-
ness at the expense of poor efficiency. To reduce the typ-
ically high execution times, we investigate how learning-
based entity resolution can be realized in a cloud infras-
tructure using MapReduce. We propose and evaluate two
efficient MapReduce-based strategies for pair-wise similar-
ity 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 effec-

The effectiveness and scalability of MapReduce-based im-
plementations of complex data-intensive tasks depend on an
even redistribution of data between map and reduce tasks.
In the presence of skewed data, sophisticated redistribution
approaches thus become necessary to achieve load balanc-
ing among all reduce tasks to be executed in parallel. For
the complex problem of entity resolution with blocking, we
propose BlockSplit, a load balancing approach that supports
blocking techniques to reduce the search space of entity res-