Generic Entity Resolution in Relational Databases

Sidló, Csaba István

doi:10.1007/978-3-642-03973-7_6

Cited by 5 publications

(2 citation statements)

References 14 publications

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“…[1] adapts the algorithms to a distributed environment. Our generic ER algorithms for relational databases were published in [35].…”

Section: Related Workmentioning

confidence: 99%

“…We applied the 1st feature combination of Section 7.1, not using the address and phone number attributes, and using strict attribute equality-based feature match. Figure 2 also provides an inaccurate, but useful overview of several ER algorithms on client data, namely the following: -Java-F-Swoosh: a Java implementation of the best previously known generic entity resolution algorithm (F-Swoosh [2]), -DB-GER: our best relational entity resolution algorithm, based on a commercial relational database (see [35]), -index-ER-BDB: our best efficient indexing algorithm built on Java and Berkeley DB (see [36]), -MapReduce: our best distributed ER algorithm using 15 computer nodes and Hadoop implementation.…”

Section: Scalabilitymentioning

confidence: 99%

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Flexible and Efficient Distributed Resolution of Large Entities

Molnár

Benczúr

Sidló

2012

Lecture Notes in Computer Science

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Abstract. 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.

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“…[1] adapts the algorithms to a distributed environment. Our generic ER algorithms for relational databases were published in [35].…”

Section: Related Workmentioning

confidence: 99%

Section: Scalabilitymentioning

confidence: 99%