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Learning-based entity resolution with MapReduce by Lars Kolb; Hanna Köpcke; Andreas Thor; Erhard Rahm is a scholarly article available to read on EtoBox.

What is Learning-based entity resolution with MapReduce about?

Entity resolution is a crucial step for data quality and data integration. Learning-based approaches show high effectiveness at the expense of poor efficiency. To reduce the typically high execution times, we investigate how learningbased entity resolution can be realized in a cloud infrastructure using MapReduce. We propose and evaluate two efficient MapReduce-based strategies for pair-wise similarity 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 effectiveness of the proposed approaches.

Author

Lars Kolb; Hanna Köpcke; Andreas Thor; Erhard Rahm

Publisher

ACM

Published

2011

Language

EN