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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Flexible and Efficient Distributed Resolution of Large Entities
verfasst von : András J. Molnár, András A. Benczúr, Csaba István Sidló
Erschienen in: Foundations of Information and Knowledge Systems
Verlag: Springer Berlin Heidelberg
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Entity resolution (ER) is a computationally hard problem of data integration scenarios, where database records have to be grouped according to the real-world entities they belong to. In practice these entities may consist of only a few records from different data sources with typos or historical data. In other cases they may contain significantly more records, especially when we search for entities on a higher level of a concept hierarchy than records.
In this paper we give theoretical foundation of a variety of practically important match functions. We show that under these formulations, ER with large entities can be solved efficiently with algorithms based on MapReduce, a distributed computing paradigm. Our algorithm can efficiently incorporate probabilistic and similarity-based record match, enabling flexible match function definition. We demonstrate the usability of our model and algorithm in a real-world insurance ER scenario, where we identify household groups of client records.