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dc.contributor.authorMantle, M
dc.contributor.authorBatsakis, S
dc.contributor.authorAntoniou, G
dc.date.accessioned2019-07-04T12:35:47Z
dc.date.available2019-07-04T12:35:47Z
dc.date.issued2019
dc.identifier.issn0950-7051
dc.identifier.doi10.1016/j.knosys.2018.08.035
dc.identifier.urihttp://hdl.handle.net/10072/383446
dc.description.abstractMost of the existing work in the field of Qualitative Spatial Temporal Reasoning (QSTR) has focussed on comparatively small constraint networks that consist of hundreds or at most thousands of relations. Recently we have seen the emergence of much larger qualitative spatial knowledge graphs that feature hundreds of thousands and millions of relations. Traditional approaches to QSTR are unable to reason over networks of such size. In this article we describe ParQR, a parallel, distributed implementation of QSTR techniques that addresses the challenge of reasoning over large-scale qualitative spatial and temporal datasets. We have implemented ParQR using the Apache Spark framework, and evaluated our approach using both large scale synthetic datasets and real-world knowledge graphs. We show that our approach scales effectively, is able to handle constraint networks consisting of millions of relations, and outperforms current distributed implementations of QSTR.
dc.description.peerreviewedYes
dc.relation.ispartofpagefrom214
dc.relation.ispartofpageto226
dc.relation.ispartofjournalKnowledge-Based Systems
dc.relation.ispartofvolume163
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchCommerce, Management, Tourism and Services
dc.subject.fieldofresearchPsychology and Cognitive Sciences
dc.subject.fieldofresearchcode08
dc.subject.fieldofresearchcode15
dc.subject.fieldofresearchcode17
dc.titleLarge scale distributed spatio-temporal reasoning using real-world knowledge graphs
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.hasfulltextNo Full Text
gro.griffith.authorAntoniou, Grigorios


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