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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.

Abstract Cloud infrastructures enable the efficient parallel
execution of data-intensive tasks such as entity resolution on
large datasets. We investigate challenges and possible solu-
tions of using the MapReduce programming model for par-
allel entity resolution using Sorting Neighborhood blocking
(SN). We propose and evaluate two efficient MapReduce-
based implementations for single- and multi-pass SN that
either use multiple MapReduce jobs or apply a tailored data
replication. We also propose an automatic data partitioning
approach for multi-pass SN to achieve load balancing. Our

Cloud infrastructures enable the efficient parallel execution of data-intensive
tasks such as entity resolution on large datasets. We investigate challenges and possi-
ble solutions of using the MapReduce programming model for parallel entity resolu-
tion. 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.