The international community has made significant efforts to flatten the COVID-19 curve,including predicting transmission[1,2],executing unprecedented global lockdowns and social distancing[3,4],promoting the wearing o...The international community has made significant efforts to flatten the COVID-19 curve,including predicting transmission[1,2],executing unprecedented global lockdowns and social distancing[3,4],promoting the wearing of facemasks and social distancing measures[5],and isolating confirmed cases and contacts[6].Because of the adverse consequences of these lockdown measures[7],many cities have reopened so they can rebuild their economies.However,as mobility has gradually returned towards normal,imported cases from unknown sources have disrupted the recovery situation,and cities are continually at high risk of new waves of infection[8,9]since airborne transmission is the dominant transmission route[10].展开更多
Geospatial Semantic Web promises better retrieval geospatial information for Digital Earth systems by explicitly representing the semantics of data through ontologies.It also promotes sharing and reuse of geospatial d...Geospatial Semantic Web promises better retrieval geospatial information for Digital Earth systems by explicitly representing the semantics of data through ontologies.It also promotes sharing and reuse of geospatial data by encoding it in Semantic Web languages,such as RDF,to form geospatial knowledge base.For many applications,rapid retrieval of spatial data from the knowledge base is critical.However,spatial data retrieval using the standard Semantic Web query language–Geo-SPARQL–can be very inefficient because the data in the knowledge base are no longer indexed to support efficient spatial queries.While recent research has been devoted to improving query performance on general knowledge base,it is still challenging to support efficient query of the spatial data with complex topological relationships.This research introduces a query strategy to improve the query performance of geospatial knowledge base by creating spatial indexing on-the-fly to prune the search space for spatial queries and by parallelizing the spatial join computations within the queries.We focus on improving the performance of Geo-SPARQL queries on knowledge bases encoded in RDF.Our initial experiments show that the proposed strategy can greatly reduce the runtime costs of Geo-SPARQL query through on-the-fly spatial indexing and parallel execution.展开更多
基金support from the National Research FoundationPrime Minister’s Office+7 种基金Singapore under its Campus for Research Excellence and Technological Enterprise(CREATE)programmeThe Hong Kong Polytechnic University Strategic Hiring Scheme(P0036221)support from the Key Program of National Natural Science Foundation of China(41930648)supports from the Hong Kong Research Grants Council(15602619,15603920,and C7064-18GF)supports from the Hong Kong Research Grants Council(14605920,14611621,and C4023-20GF)support from the National University of SingaporeMinistry of Education,Tier 1 under WBS R-109-000-270-133Ministry of Natural Resources of the People’s Republic of China(GS(2021)7327)。
文摘The international community has made significant efforts to flatten the COVID-19 curve,including predicting transmission[1,2],executing unprecedented global lockdowns and social distancing[3,4],promoting the wearing of facemasks and social distancing measures[5],and isolating confirmed cases and contacts[6].Because of the adverse consequences of these lockdown measures[7],many cities have reopened so they can rebuild their economies.However,as mobility has gradually returned towards normal,imported cases from unknown sources have disrupted the recovery situation,and cities are continually at high risk of new waves of infection[8,9]since airborne transmission is the dominant transmission route[10].
基金Anselin’s research was supported in part by award OCI-1047916,SI2-SSI from the US National Science Foundation.
文摘Geospatial Semantic Web promises better retrieval geospatial information for Digital Earth systems by explicitly representing the semantics of data through ontologies.It also promotes sharing and reuse of geospatial data by encoding it in Semantic Web languages,such as RDF,to form geospatial knowledge base.For many applications,rapid retrieval of spatial data from the knowledge base is critical.However,spatial data retrieval using the standard Semantic Web query language–Geo-SPARQL–can be very inefficient because the data in the knowledge base are no longer indexed to support efficient spatial queries.While recent research has been devoted to improving query performance on general knowledge base,it is still challenging to support efficient query of the spatial data with complex topological relationships.This research introduces a query strategy to improve the query performance of geospatial knowledge base by creating spatial indexing on-the-fly to prune the search space for spatial queries and by parallelizing the spatial join computations within the queries.We focus on improving the performance of Geo-SPARQL queries on knowledge bases encoded in RDF.Our initial experiments show that the proposed strategy can greatly reduce the runtime costs of Geo-SPARQL query through on-the-fly spatial indexing and parallel execution.
