Abstract
MapReduce framework provides a new platform for data integration on distributed environment. We demonstrate a MapReduce-based entity resolution framework which efficiently solves the matching problem for structured, semi-structured and unstructured entities. We propose a random-based data representation method for reducing network transmission; we implement our design on MapReduce and design two solutions for reducing redundant comparisons. Our demo provides an easy-to-use platform for entity matching and performance analysis. We also compare the performance of our algorithm with the state-of-the-art blocking-based methods.
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© 2015 Springer International Publishing Switzerland
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Chao, P., Li, Y., Gao, Z., Fang, J., He, X., Zhang, R. (2015). SEMI: A Scalable Entity Matching System Based on MapReduce. In: Sharaf, M., Cheema, M., Qi, J. (eds) Databases Theory and Applications. ADC 2015. Lecture Notes in Computer Science(), vol 9093. Springer, Cham. https://doi.org/10.1007/978-3-319-19548-3_29
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DOI: https://doi.org/10.1007/978-3-319-19548-3_29
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