Dimensional Inconsistency Measures and Postulates in Spatio-Temporal Databases

Autores
Grant, John; Martinez, Maria Vanina; Molinaro, Cristian; Parisi, Francesco
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The problem of managing spatio-temporal data arises in many applications, such as location-based services, environmental monitoring, geographic information systems, and many others. Often spatio-temporal data arising from such applications turn out to be inconsistent, i.e., representing an impossible situation in the real world. Though several inconsistency measures have been proposed to quantify in a principled way inconsistency in propositional knowledge bases, little effort has been done so far on inconsistency measures tailored for the spatio-temporal setting.In this paper, we define and investigate new measures that are particularly suitable for dealing with inconsistent spatio-temporal information, because they explicitly take into account the spatial and temporal dimensions, as well as the dimension concerning the identifiers of the monitored objects. Specifically, we first define natural measures that look at individual dimensions (time, space, and objects), and then propose measures based on the notion of a repair. We then analyze their behavior w.r.t. common postulates defined for classical propositional knowledge bases, and find that the latter are not suitable for spatio-temporal databases, in that the proposed inconsistency measures do not often satisfy them. In light of this, we argue that also postulates should explicitly take into account the spatial, temporal, and object dimensions and thus define ?dimension-aware? counterparts of common postulates, which are indeed often satisfied by the new inconsistency measures. Finally, we study the complexity of the proposed inconsistency measures.
Fil: Grant, John. Towson University; Estados Unidos
Fil: Martinez, Maria Vanina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina
Fil: Molinaro, Cristian. Università della Calabria; Italia
Fil: Parisi, Francesco. Università della Calabria; Italia
Materia
knowledge representation
spatial and temporal databases
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/174160
Acceder
id CONICETDig_10a092c606ba52ac342992bdb80ad23b
oai_identifier_str oai:ri.conicet.gov.ar:11336/174160
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Dimensional Inconsistency Measures and Postulates in Spatio-Temporal DatabasesGrant, JohnMartinez, Maria VaninaMolinaro, CristianParisi, Francescoknowledge representationspatial and temporal databaseshttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1The problem of managing spatio-temporal data arises in many applications, such as location-based services, environmental monitoring, geographic information systems, and many others. Often spatio-temporal data arising from such applications turn out to be inconsistent, i.e., representing an impossible situation in the real world. Though several inconsistency measures have been proposed to quantify in a principled way inconsistency in propositional knowledge bases, little effort has been done so far on inconsistency measures tailored for the spatio-temporal setting.In this paper, we define and investigate new measures that are particularly suitable for dealing with inconsistent spatio-temporal information, because they explicitly take into account the spatial and temporal dimensions, as well as the dimension concerning the identifiers of the monitored objects. Specifically, we first define natural measures that look at individual dimensions (time, space, and objects), and then propose measures based on the notion of a repair. We then analyze their behavior w.r.t. common postulates defined for classical propositional knowledge bases, and find that the latter are not suitable for spatio-temporal databases, in that the proposed inconsistency measures do not often satisfy them. In light of this, we argue that also postulates should explicitly take into account the spatial, temporal, and object dimensions and thus define ?dimension-aware? counterparts of common postulates, which are indeed often satisfied by the new inconsistency measures. Finally, we study the complexity of the proposed inconsistency measures.Fil: Grant, John. Towson University; Estados UnidosFil: Martinez, Maria Vanina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; ArgentinaFil: Molinaro, Cristian. Università della Calabria; ItaliaFil: Parisi, Francesco. Università della Calabria; ItaliaAI Access Foundation2021-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/174160Grant, John; Martinez, Maria Vanina; Molinaro, Cristian; Parisi, Francesco; Dimensional Inconsistency Measures and Postulates in Spatio-Temporal Databases; AI Access Foundation; Journal of Artificial Intelligence Research; 71; 8-2021; 733-7801076-9757CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.jair.org/index.php/jair/article/view/12435info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-29T11:39:03Zoai:ri.conicet.gov.ar:11336/174160instacron: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:34982025-10-29 11:39:03.317CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Dimensional Inconsistency Measures and Postulates in Spatio-Temporal Databases
title Dimensional Inconsistency Measures and Postulates in Spatio-Temporal Databases
spellingShingle Dimensional Inconsistency Measures and Postulates in Spatio-Temporal Databases
Grant, John
knowledge representation
spatial and temporal databases
title_short Dimensional Inconsistency Measures and Postulates in Spatio-Temporal Databases
title_full Dimensional Inconsistency Measures and Postulates in Spatio-Temporal Databases
title_fullStr Dimensional Inconsistency Measures and Postulates in Spatio-Temporal Databases
title_full_unstemmed Dimensional Inconsistency Measures and Postulates in Spatio-Temporal Databases
title_sort Dimensional Inconsistency Measures and Postulates in Spatio-Temporal Databases
dc.creator.none.fl_str_mv Grant, John
Martinez, Maria Vanina
Molinaro, Cristian
Parisi, Francesco
author Grant, John
author_facet Grant, John
Martinez, Maria Vanina
Molinaro, Cristian
Parisi, Francesco
author_role author
author2 Martinez, Maria Vanina
Molinaro, Cristian
Parisi, Francesco
author2_role author
author
author
dc.subject.none.fl_str_mv knowledge representation
spatial and temporal databases
topic knowledge representation
spatial and temporal databases
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The problem of managing spatio-temporal data arises in many applications, such as location-based services, environmental monitoring, geographic information systems, and many others. Often spatio-temporal data arising from such applications turn out to be inconsistent, i.e., representing an impossible situation in the real world. Though several inconsistency measures have been proposed to quantify in a principled way inconsistency in propositional knowledge bases, little effort has been done so far on inconsistency measures tailored for the spatio-temporal setting.In this paper, we define and investigate new measures that are particularly suitable for dealing with inconsistent spatio-temporal information, because they explicitly take into account the spatial and temporal dimensions, as well as the dimension concerning the identifiers of the monitored objects. Specifically, we first define natural measures that look at individual dimensions (time, space, and objects), and then propose measures based on the notion of a repair. We then analyze their behavior w.r.t. common postulates defined for classical propositional knowledge bases, and find that the latter are not suitable for spatio-temporal databases, in that the proposed inconsistency measures do not often satisfy them. In light of this, we argue that also postulates should explicitly take into account the spatial, temporal, and object dimensions and thus define ?dimension-aware? counterparts of common postulates, which are indeed often satisfied by the new inconsistency measures. Finally, we study the complexity of the proposed inconsistency measures.
Fil: Grant, John. Towson University; Estados Unidos
Fil: Martinez, Maria Vanina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigación en Ciencias de la Computación. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigación en Ciencias de la Computación; Argentina
Fil: Molinaro, Cristian. Università della Calabria; Italia
Fil: Parisi, Francesco. Università della Calabria; Italia
description The problem of managing spatio-temporal data arises in many applications, such as location-based services, environmental monitoring, geographic information systems, and many others. Often spatio-temporal data arising from such applications turn out to be inconsistent, i.e., representing an impossible situation in the real world. Though several inconsistency measures have been proposed to quantify in a principled way inconsistency in propositional knowledge bases, little effort has been done so far on inconsistency measures tailored for the spatio-temporal setting.In this paper, we define and investigate new measures that are particularly suitable for dealing with inconsistent spatio-temporal information, because they explicitly take into account the spatial and temporal dimensions, as well as the dimension concerning the identifiers of the monitored objects. Specifically, we first define natural measures that look at individual dimensions (time, space, and objects), and then propose measures based on the notion of a repair. We then analyze their behavior w.r.t. common postulates defined for classical propositional knowledge bases, and find that the latter are not suitable for spatio-temporal databases, in that the proposed inconsistency measures do not often satisfy them. In light of this, we argue that also postulates should explicitly take into account the spatial, temporal, and object dimensions and thus define ?dimension-aware? counterparts of common postulates, which are indeed often satisfied by the new inconsistency measures. Finally, we study the complexity of the proposed inconsistency measures.
publishDate 2021
dc.date.none.fl_str_mv 2021-08
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/11336/174160
Grant, John; Martinez, Maria Vanina; Molinaro, Cristian; Parisi, Francesco; Dimensional Inconsistency Measures and Postulates in Spatio-Temporal Databases; AI Access Foundation; Journal of Artificial Intelligence Research; 71; 8-2021; 733-780
1076-9757
CONICET Digital
CONICET
url http://hdl.handle.net/11336/174160
identifier_str_mv Grant, John; Martinez, Maria Vanina; Molinaro, Cristian; Parisi, Francesco; Dimensional Inconsistency Measures and Postulates in Spatio-Temporal Databases; AI Access Foundation; Journal of Artificial Intelligence Research; 71; 8-2021; 733-780
1076-9757
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.jair.org/index.php/jair/article/view/12435
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv AI Access Foundation
publisher.none.fl_str_mv AI Access Foundation
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_ 1847426245974818816
score 13.10058

Publicaciones similares