  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
in-put entities and affinity-based scheduling of match tasks.
