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dc.contributor.author
Grant, John
dc.contributor.author
Martinez, Maria Vanina
dc.contributor.author
Molinaro, Cristian
dc.contributor.author
Parisi, Francesco
dc.date.available
2022-10-20T14:30:07Z
dc.date.issued
2021-08
dc.identifier.citation
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
dc.identifier.issn
1076-9757
dc.identifier.uri
http://hdl.handle.net/11336/174160
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
AI Access Foundation
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
knowledge representation
dc.subject
spatial and temporal databases
dc.subject.classification
Ciencias de la Computación
dc.subject.classification
Ciencias de la Computación e Información
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Dimensional Inconsistency Measures and Postulates in Spatio-Temporal Databases
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2022-09-22T16:15:41Z
dc.journal.volume
71
dc.journal.pagination
733-780
dc.journal.pais
Estados Unidos
dc.description.fil
Fil: Grant, John. Towson University; Estados Unidos
dc.description.fil
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
dc.description.fil
Fil: Molinaro, Cristian. Università della Calabria; Italia
dc.description.fil
Fil: Parisi, Francesco. Università della Calabria; Italia
dc.journal.title
Journal of Artificial Intelligence Research
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.jair.org/index.php/jair/article/view/12435
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