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

Federico del Razo Lopez 1 Stephane Sanchez 1 Anne Laurent 2 Pascal Poncelet 2 Maguelonne Teisseire 2
1 LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
2 TATOO - Fouille de données environnementales
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
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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https://hal-lirmm.ccsd.cnrs.fr/lirmm-00272444
Contributor : Anne Laurent <>
Submitted on : Friday, April 11, 2008 - 2:52:55 PM
Last modification on : Thursday, June 3, 2021 - 3:32:11 PM

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  • HAL Id : lirmm-00272444, version 1

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CNRS | LIRMM | MIPS | UNIV-MONTPELLIER

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Federico del Razo Lopez, Stephane 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, University of Zielona Góra 2008, 1, pp.21. ⟨lirmm-00272444⟩

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