Efficient and Effective Duplicate Detection in Hierarchical Data

Luís Leitão 1 Pável Calado 1 Melanie Herschel 2, 3, *
* Corresponding author
2 OAK - Database optimizations and architectures for complex large data
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
3 BD
LRI - Laboratoire de Recherche en Informatique
Abstract : Although there is a long line of work on identifying duplicates in relational data, only a few solutions focus on duplicate detection in more complex hierarchical structures, like XML data. In this paper, we present a novel method for XML duplicate detection, called XMLDup. XMLDup uses a Bayesian network to determine the probability of two XML elements being duplicates, considering not only the information within the elements, but also the way that information is structured. In addition, to improve the efficiency of the network evaluation, a novel pruning strategy, capable of significant gains over the unoptimized version of the algorithm, is presented. Through experiments, we show that our algorithm is able to achieve high precision and recall scores in several datasets. XMLDup is also able to outperform another state of the art duplicate detection solution, both in terms of efficiency and of effectiveness.
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Luís Leitão, Pável Calado, Melanie Herschel. Efficient and Effective Duplicate Detection in Hierarchical Data. IEEE Transactions on Knowledge and Data Engineering, Institute of Electrical and Electronics Engineers, 2012, 99 (PrePrints), ⟨10.1109/TKDE.2012.60⟩. ⟨hal-00722505⟩

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