Data Access over Large Semi-Structured Databases A Generic Approach Towards Rule-Based Systems - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier
Theses Year : 2014

Data Access over Large Semi-Structured Databases A Generic Approach Towards Rule-Based Systems

Bruno Paiva Lima da Silva
  • Function : Author
  • PersonId : 908991
Graphs for Inferences on Knowledge

Abstract

This thesis presents original research in the field of Knowledge Representation and Reasoning, a central Artificial Intelligence issue.The contribution of the thesis is providing the ALASKA platform, that is able to integrateunder a unified logical framework different implementation approaches for addressing the RULE-BASED DATA ACCESS problem.
This thesis presents original research in the field of Knowledge Representation and Reasoning,a central Artificial Intelligence issue.The contribution of the thesis is providing the ALASKA platform, that is able to integrateunder a unified logical framework different implementation approaches for addressing the RULE-BASED DATA ACCESS problem.

Domains

Artificial Intelligence [cs.AI]
Fichier principal
Vignette du fichier
phdthesis-PaivaLimadaSilva.pdf (729.69 Ko) Télécharger le fichier

Madalina Croitoru  : Connect in order to contact the contributor

https://hal-lirmm.ccsd.cnrs.fr/tel-01088101

Submitted on : Thursday, November 27, 2014-1:45:40 PM

Last modification on : Thursday, November 28, 2024-3:29:41 AM

Long-term archiving on : Friday, April 14, 2017-10:15:18 PM

Download

Dates and versions

tel-01088101 , version 1 (27-11-2014)

Identifiers

  • HAL Id : tel-01088101 , version 1

Cite

Bruno Paiva Lima da Silva. Data Access over Large Semi-Structured Databases A Generic Approach Towards Rule-Based Systems. Artificial Intelligence [cs.AI]. Universite Montpellier 2, 2014. English. ⟨NNT : ⟩. ⟨tel-01088101⟩

Export

BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite

Collections

CNRS INRIA GRAPHIK LIRMM INRIA2 MIPS UNIV-MONTPELLIER THESES-UNC
566 View
392 Download

Share

More