Data Structures for Efficient Tree Mining: From Crisp to Soft Embedding Constraints

Abstract : XML is playing an increasing role in data exchanges and the volume of available resources is thus growing dramatically. As they are heterogeneous, these resources must be translated into a {\em mediator} schema to be queried. For this purpose, automatic tools are required. These tools must allow the extraction of common data structures from the tree-like XML data. In this paper, we present a novel approach based on a low memory-consuming representation which can be improved by considering a binary representation. We show that these representations have many properties to enhance subtree mining algorithms, especially when considering soft tree embedding constraints. Experiments highlight the interest of our proposition.
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Article dans une revue
International Journal of Applied Mathematics and Computer Science, De Gruyter, 2008, 1, pp.21
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00272444
Contributeur : Anne Laurent <>
Soumis le : vendredi 11 avril 2008 - 14:52:55
Dernière modification le : jeudi 24 mai 2018 - 15:59:23

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  • HAL Id : lirmm-00272444, version 1

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Federico Del Razo Lopez, Stéphane Sanchez, Anne Laurent, Pascal Poncelet, Maguelonne Teisseire. Data Structures for Efficient Tree Mining: From Crisp to Soft Embedding Constraints. International Journal of Applied Mathematics and Computer Science, De Gruyter, 2008, 1, pp.21. 〈lirmm-00272444〉

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