Abstract
Object matching is a traditional deduplication approach that is used to identify the duplicates within one or several data sets respectively. However, this task will become difficult in the context of big data. Few publications have mentioned incremental methods to solve this legacy issue in parallel. To address this limitation, we aim to propose an incremental object matching approach (IOMMapReduce) in parallel. We investigate possible solutions to improve current object matching approaches with MapReduce, to make it support incrementality to speed up the deduplication process. Finally, our experimental evaluation on large data sets shows the high effectiveness and efficiency of the proposed approaches.
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