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Scalable Iterative Graph Duplicate Detection
Nov. 2012 (vol. 24 no. 11)
pp. 2094-2108
Melanie Herschel, University of Tübingen, Tübungen
Felix Naumann, University of Potsdam, Potsdam
Sascha Szott, Konrad-Zuse-Zentrum für Informationstechnik, Berlin
Maik Taubert, Biotronik SE & Co. KG, Berlin
Duplicate detection determines different representations of real-world objects in a database. Recent research has considered the use of relationships among object representations to improve duplicate detection. In the general case where relationships form a graph, research has mainly focused on duplicate detection quality/effectiveness. Scalability has been neglected so far, even though it is crucial for large real-world duplicate detection tasks. We scale-up duplicate detection in graph data (ddg) to large amounts of data and pairwise comparisons, using the support of a relational database management system. To this end, we first present a framework that generalizes the ddg process. We then present algorithms to scale ddg in space (amount of data processed with bounded main memory) and in time. Finally, we extend our framework to allow batched and parallel ddg, thus further improving efficiency. Experiments on data of up to two orders of magnitude larger than data considered so far in ddg show that our methods achieve the goal of scaling ddg to large volumes of data.
Index Terms:
Motion pictures,Sorting,Databases,Scalability,Classification algorithms,Image edge detection,Runtime,parallelization,Duplicate detection,data cleaning,data integration,record linkage,entity resolution,scalability
Citation:
Melanie Herschel, Felix Naumann, Sascha Szott, Maik Taubert, "Scalable Iterative Graph Duplicate Detection," IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 11, pp. 2094-2108, Nov. 2012, doi:10.1109/TKDE.2011.99
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