Reducing the gap between experts' knowledge and data: the TOM4D methodology

Autores
Pomponio, Laura Matilde; Le Goc, Marc
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Dynamic process modelling is generally accomplished from experts' knowledge through Knowledge Engineering (KE); however, the obtained models are sometimes deficient for interpreting the input data flow coming from the real process evolution perceived through sensors. This shortcoming lies in specialists' tacit knowledge, difficult to elicit, and in that certain process phenomena are unknown or unforeseen to experts. An alternative to complement the modelling task is to resort to a Knowledge Discovery in Database (KDD) process. Nevertheless, most KE approaches do not address the processing of knowledge obtained from data. This work proposes a KE methodology called Timed Observation Modelling For Diagnosis (TOM4D) which allows building dynamic process models from experts' knowledge and data where the obtained models can be compared and combined with models obtained through a KDD process in order to define a model more suitable to the dynamic process reality.
Fil: Pomponio, Laura Matilde. Laboratoire des Sciences de l; Francia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; Argentina
Fil: Le Goc, Marc. Laboratoire des Sciences de l; Francia
Materia
Methodologies And Tools
Data And Knowledge
Knowledge Engineering
Knowledge Modelling
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/15164
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/15164
network_acronym_str CONICETDig
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network_name_str CONICET Digital (CONICET)
spelling Reducing the gap between experts' knowledge and data: the TOM4D methodologyPomponio, Laura MatildeLe Goc, MarcMethodologies And ToolsData And KnowledgeKnowledge EngineeringKnowledge Modellinghttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Dynamic process modelling is generally accomplished from experts' knowledge through Knowledge Engineering (KE); however, the obtained models are sometimes deficient for interpreting the input data flow coming from the real process evolution perceived through sensors. This shortcoming lies in specialists' tacit knowledge, difficult to elicit, and in that certain process phenomena are unknown or unforeseen to experts. An alternative to complement the modelling task is to resort to a Knowledge Discovery in Database (KDD) process. Nevertheless, most KE approaches do not address the processing of knowledge obtained from data. This work proposes a KE methodology called Timed Observation Modelling For Diagnosis (TOM4D) which allows building dynamic process models from experts' knowledge and data where the obtained models can be compared and combined with models obtained through a KDD process in order to define a model more suitable to the dynamic process reality.Fil: Pomponio, Laura Matilde. Laboratoire des Sciences de l; Francia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; ArgentinaFil: Le Goc, Marc. Laboratoire des Sciences de l; FranciaElsevier Science2014-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/15164Pomponio, Laura Matilde; Le Goc, Marc; Reducing the gap between experts' knowledge and data: the TOM4D methodology; Elsevier Science; Data & Knowledge Engineering; 94; Part A; 11-2014; 1-370169-023Xenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.datak.2014.07.006info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0169023X14000652info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:09:16Zoai:ri.conicet.gov.ar:11336/15164instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 10:09:16.84CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Reducing the gap between experts' knowledge and data: the TOM4D methodology
title Reducing the gap between experts' knowledge and data: the TOM4D methodology
spellingShingle Reducing the gap between experts' knowledge and data: the TOM4D methodology
Pomponio, Laura Matilde
Methodologies And Tools
Data And Knowledge
Knowledge Engineering
Knowledge Modelling
title_short Reducing the gap between experts' knowledge and data: the TOM4D methodology
title_full Reducing the gap between experts' knowledge and data: the TOM4D methodology
title_fullStr Reducing the gap between experts' knowledge and data: the TOM4D methodology
title_full_unstemmed Reducing the gap between experts' knowledge and data: the TOM4D methodology
title_sort Reducing the gap between experts' knowledge and data: the TOM4D methodology
dc.creator.none.fl_str_mv Pomponio, Laura Matilde
Le Goc, Marc
author Pomponio, Laura Matilde
author_facet Pomponio, Laura Matilde
Le Goc, Marc
author_role author
author2 Le Goc, Marc
author2_role author
dc.subject.none.fl_str_mv Methodologies And Tools
Data And Knowledge
Knowledge Engineering
Knowledge Modelling
topic Methodologies And Tools
Data And Knowledge
Knowledge Engineering
Knowledge Modelling
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Dynamic process modelling is generally accomplished from experts' knowledge through Knowledge Engineering (KE); however, the obtained models are sometimes deficient for interpreting the input data flow coming from the real process evolution perceived through sensors. This shortcoming lies in specialists' tacit knowledge, difficult to elicit, and in that certain process phenomena are unknown or unforeseen to experts. An alternative to complement the modelling task is to resort to a Knowledge Discovery in Database (KDD) process. Nevertheless, most KE approaches do not address the processing of knowledge obtained from data. This work proposes a KE methodology called Timed Observation Modelling For Diagnosis (TOM4D) which allows building dynamic process models from experts' knowledge and data where the obtained models can be compared and combined with models obtained through a KDD process in order to define a model more suitable to the dynamic process reality.
Fil: Pomponio, Laura Matilde. Laboratoire des Sciences de l; Francia. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Rosario. Centro Internacional Franco Argentino de Ciencias de la Información y Sistemas; Argentina. Universidad Nacional de Rosario; Argentina
Fil: Le Goc, Marc. Laboratoire des Sciences de l; Francia
description Dynamic process modelling is generally accomplished from experts' knowledge through Knowledge Engineering (KE); however, the obtained models are sometimes deficient for interpreting the input data flow coming from the real process evolution perceived through sensors. This shortcoming lies in specialists' tacit knowledge, difficult to elicit, and in that certain process phenomena are unknown or unforeseen to experts. An alternative to complement the modelling task is to resort to a Knowledge Discovery in Database (KDD) process. Nevertheless, most KE approaches do not address the processing of knowledge obtained from data. This work proposes a KE methodology called Timed Observation Modelling For Diagnosis (TOM4D) which allows building dynamic process models from experts' knowledge and data where the obtained models can be compared and combined with models obtained through a KDD process in order to define a model more suitable to the dynamic process reality.
publishDate 2014
dc.date.none.fl_str_mv 2014-11
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/15164
Pomponio, Laura Matilde; Le Goc, Marc; Reducing the gap between experts' knowledge and data: the TOM4D methodology; Elsevier Science; Data & Knowledge Engineering; 94; Part A; 11-2014; 1-37
0169-023X
url http://hdl.handle.net/11336/15164
identifier_str_mv Pomponio, Laura Matilde; Le Goc, Marc; Reducing the gap between experts' knowledge and data: the TOM4D methodology; Elsevier Science; Data & Knowledge Engineering; 94; Part A; 11-2014; 1-37
0169-023X
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.datak.2014.07.006
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0169023X14000652
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier Science
publisher.none.fl_str_mv Elsevier Science
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1842270074145079296
score 13.13397

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