Efficient and Effective Duplicate Detection in Hierarchical Data
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. Finally, we also study how important the structure of elements is in the duplicate detection process. We observe that, not only structure can clearly influence the outcome, but also that, by ensuring a structure that is adequate to the characteristics of the data, we can actually improve the quality of the results.