Abstract
We consider the problem of answering similarity join queries on large, non-schematic, heterogeneous XML datasets. Realizing similarity joins on such datasets is challenging, because the semi-structured nature of XML substantially increases the complexity of the underlying similarity function in terms of both effectiveness and efficiency. Moreover, even the selection of pieces of information for similarity assessment is complicated because these can appear at different parts among documents in a dataset. In this paper, we present an approach that jointly calculates textual and structural similarity of XML trees while implicitly embedding similarity selection into join processing. We validate the accuracy, performance, and scalability of our techniques with a set of experiments in the context of an XML DBMS.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Elmagarmid, A.K., Ipeirotis, P.G., Verykios, V.S.: Duplicate record detection: A survey. TKDE 19(1), 1–16 (2007)
Ribeiro, L.A., Härder, T.: Generalizing prefix filtering to improve set similarity joins. Information Systems 36(1), 62–78 (2011)
Ribeiro, L.A., Härder, T., Pimenta, F.S.: A cluster-based approach to xml similarity joins. In: IDEAS, pp. 182–193 (2009)
Mathis, C.: Storing, Indexing, and Querying XML Documents in Native XML Database Systems. PhD thesis, Technische Universität Kaiserslautern (2009)
Tai, K.C.: The tree-to-tree correction problem. Journal of the ACM 26(3), 422–433 (1979)
Ribeiro, L., Härder, T.: Evaluating performance and quality of XML-based similarity joins. In: Atzeni, P., Caplinskas, A., Jaakkola, H. (eds.) ADBIS 2008. LNCS, vol. 5207, pp. 246–261. Springer, Heidelberg (2008)
Augsten, N., Böhlen, M.H., Gamper, J.: The pq-gram distance between ordered labeled trees. TODS 35(1) (2010)
Demaine, E.D., Mozes, S., Rossman, B., Weimann, O.: An optimal decomposition algorithm for tree edit distance. In: Arge, L., Cachin, C., Jurdziński, T., Tarlecki, A. (eds.) ICALP 2007. LNCS, vol. 4596, pp. 146–157. Springer, Heidelberg (2007)
Guha, S., Jagadish, H.V., Koudas, N., Srivastava, D., Yu, T.: Integrating xml data sources using approximate joins. TODS 31(1), 161–207 (2006)
Augsten, N., Böhlen, M.H., Dyreson, C.E., Gamper, J.: Approximate joins for data-centric xml. In: ICDE, pp. 814–823 (2008)
Weis, M., Naumann, F.: Dogmatix tracks down duplicates in xml. In: SIGMOD, pp. 431–442 (2005)
Dalamagas, T., Cheng, T., Winkel, K.J., Sellis, T.K.: A methodology for clustering xml documents by structure. Information Systems 31(3), 187–228 (2006)
Joshi, S., Agrawal, N., Krishnapuram, R., Negi, S.: A bag of paths model for measuring structural similarity in web documents. In: SIGKDD, pp. 577–582 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ribeiro, L.A., Härder, T. (2011). Ingredients for Accurate, Fast, and Robust XML Similarity Joins. In: Hameurlain, A., Liddle, S.W., Schewe, KD., Zhou, X. (eds) Database and Expert Systems Applications. DEXA 2011. Lecture Notes in Computer Science, vol 6861. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23091-2_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-23091-2_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23090-5
Online ISBN: 978-3-642-23091-2
eBook Packages: Computer ScienceComputer Science (R0)