{"type":"ScholarlyArticle","title":"Dimensional Inconsistency Measures and Postulates in Spatio-Temporal Databases (Extended Abstract)","authors":[{"name":"John Grant","slug":"john-grant","given":"John","family":"Grant","affiliation":"Department of Computer Science and UMIACS , University of Maryland , USA"},{"name":"Maria Vanina Martinez","slug":"maria-vanina-martinez","given":"María Vanina","family":"Martínez","affiliation":"Department of Computer Science , University of Buenos Aires , Argentina; Artificial Intelligence Research"},{"name":"Cristian Molinaro","slug":"cristian-molinaro","given":"Cristian","family":"Molinaro","affiliation":"DIMES Department , University of Calabria , Italy"},{"name":"Francesco Parisi","slug":"francesco-parisi","given":"Francesco","family":"Parisi","affiliation":"DIMES Department , University of Calabria , Italy"}],"abstract":"We define and investigate new inconsistency measures that are particularly suitable for dealing with inconsistent spatio-temporal information, as 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.","doi":"10.24963/ijcai.2022/802","url":"https://ijcai.org/proceedings/2022/802","pdf":"https://ijcai.org/proceedings/2022/0802.pdf","markdown":"https://ijcai.org/proceedings/2022/802.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/802","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Journal Track","track":"Journal Track","number":802,"pages":{"first":5742,"last":5746},"keywords":["Knowledge Representation and Reasoning: Qualitative, Geometric, Spatial, Temporal Reasoning"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987236","length_sec":89,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985379","length_sec":656,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Agarwal et al., 2010] Deepak Agarwal, Datong Chen, Long ji Lin, Jayavel Shanmugasundaram, and Erik Vee. 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