Abstract
Because of the increasing volume of autonomously collected data objects, duplicate detection is an important challenge in today's data management. To evaluate the efficiency of duplicate detection algorithms with respect to big data, large test data sets are required. Existing test data generation tools, however, are either not able to produce large test data sets or are domain-dependent which limits their usefulness to a few cases. In this paper, we describe a new framework that can be used to pollute a clean, homogeneous and large data set from an arbitrary domain with duplicates, errors and inhomogeneities. To prove its concept, we implemented a prototype which is built upon the cluster computing framework Apache Spark and evaluate its performance in several experiments.
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