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Extend tree edit distance for effective object identification

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Abstract

Similarity join on XML documents which are usually modeled as rooted ordered labeled trees is widely applied, due to the ambiguity of references to the real-world objects. The conventional method dealing with this issue is based on tree edit distance, which is shortage of flexibility and efficiency. In this paper, we propose two novel edit operations together with extended tree edit distance, which can achieve good performance in similarity matching with hierarchical data structures [the run-time is \(O(n^{3})\) in the worst case]. And then, we propose \(k\)-generation set distance as a good approximation of the tree edit distance to further improve the join efficiency with quadric time complexity. Experiments on real and synthetic databases demonstrate the benefit of our method in efficiency and scalability.

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Acknowledgments

This paper was partially supported by NGFR 973 Grant 2012CB316200, NSFC Grant 61472099 and National Sci-Tech Support Plan 2015BAH10F00.

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Authors and Affiliations

  1. The School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

    Yue Wang, Hongzhi Wang, Yang Wang, Jianzhong Li & Hong Gao

  2. Donald Bren School of Information and Computer Sciences, University of California, Irvine, CA, USA

    Liyan Zhang

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  1. Yue Wang
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  2. Hongzhi Wang
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  3. Liyan Zhang
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  4. Yang Wang
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  5. Jianzhong Li
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  6. Hong Gao
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Correspondence to Hongzhi Wang.

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Wang, Y., Wang, H., Zhang, L. et al. Extend tree edit distance for effective object identification. Knowl Inf Syst 46, 629–656 (2016). https://doi.org/10.1007/s10115-014-0816-1

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