Résultat de la recherche
Formalized modeling of qualitative case studies
Cette thèse vise à résoudre les problèmes de modélisation et de traitement émergeant dans les études de cas qualitatives. Fondée sur la grounded theory, une méthode complète est proposée, initialement élaborée sous la forme d'une solution de workflow. Une combinaison d'ontologies est proposée servant à la représentation des connaissances, l'intégration et l'extraction. Sur la base de cette connaissance, l'analyse du sujet est menée afin de découvrir les informations latentes sur les documents originaux afin de représenter les thèmes implicites et mettre en place des structures hiérarchiques complexes de ces sujets. Avec les structures construites, les inférences basées sur l'ontologie est effectuée pour produire de nouveaux faits afin d'aider les tâches du domaine. Cette méthode est applicable dans la plage de multilinguisme y compris les langues non alphabétiques comme le chinois. Des expériences testées sur l'outil mis en place montrent que la méthode proposée donne des résultats satisfaisants par rapport aux méthodes existantes. La thèse générale apporte une solution nouvelle et complète pour étudier et analyser profondément des textes d'étude de cas avec une méthode qualitative. Ces résultats apportent une série d'avantages pour plusieurs domaines qui partagent la même essence du point de vue du traitement des données., This thesis aims to solve the problems of data modelization and processing emerging in qualitative case studies. Established on grounded theory, a comprehensive method is proposed, initially elaborated in the form of a workflow solution. A suit of ontologies are proposed serving for knowledge representation, integration, and extraction. Based on this knowledge, topic analysis is conducted to discover the latent information out of the original documents in order to depict the implicit themes and set up sophisticated hierarchical structures of these topics. With the built structures, ontology inference is carried out to produce new facts to assist domain tasks. This method is applicable within the range of multiple lingualism including non-alphabetical languages such as Chinese. Experiments tested on the implemented tool demonstrate that the proposed method offers satisfactory results compared with existing methods. The overall thesis provides a novel and complete solution to profoundly study and analyze case study text with a qualitative method. It brings a series of benefits to a couple of domains which share similar essence from the point of view of data processing.
Fuzzy Clustering based Methodology for Multidimensional Data Analysis in Computational Forensic Domain
As interdisciplinary domain requiring advanced and innovative methodologies, the computational forensics domain is characterized by data being, simultaneously, large scaled and uncertain, multidimensional and approximate. Forensic domain experts, trained to discover hidden pattern from crime data, are limited in their analysis without the assistance of a computational intelligence approach. In this paper, a methodology and an automatic procedure, ased on fuzzy set theory and designed to infer precise and intuitive expert-system-like rules from original forensic data, is described. The main steps of the methodology are detailed, as well as the experiments conducted on forensic data sets - both simulated data and real data, representing robberies and residential burglaries.
Fuzzy methods for forensic data analysis
In this paper we describe a methodology and an automatic procedure for inferring accurate and easily understandable expert-system-like rules from forensic data. This methodology is based on the fuzzy set theory. The algorithms we used are described in detail, and were tested on forensic data sets. We also present in detail some examples, which are representative for the obtained results.