About https://w3id.org/scholarlydata/inproceedings/iswc-2019-resource-228

Subject: https://w3id.org/scholarlydata/inproceedings/iswc-2019-resource-228Property: http://www.w3.org/1999/02/22-rdf-syntax-ns#typehttp://www.ontologydesignpatterns.org/ont/dul/DUL.owl#InformationObjecthttp://www.ontologydesignpatterns.org/ont/dul/DUL.owl#Objecthttp://www.ontologydesignpatterns.org/ont/dul/DUL.owl#SocialObjecthttps://w3id.org/scholarlydata/ontology/conference-ontology.owl#InProceedingshttp://purl.org/spar/fabio/ProceedingsPaperhttp://www.w3.org/2002/07/owl#Thinghttp://www.w3.org/2000/01/rdf-schema#ResourceProperty: http://www.w3.org/2000/01/rdf-schema#labelSparklify: A Scalable Software Component for Efficient evaluation of SPARQL queries over distributed RDF datasetsProperty: http://swrc.ontoware.org/ontology#abstractOne of the key traits of Big Data is its complexity in terms of representation, structure, or formats. One existing way to deal with it is offered by Semantic Web standards. Among them, RDF --which proposes to model data with triples representing edges in a graph-- has received a large success and the semantically annotated data has grown steadily towards a massive scale. Therefore, there is a need for scalable and efficient query engines capable of retrieving such information. In this paper, we propose emph{Sparklify}: a scalable software component for efficient evaluation of SPARQL queries over distributed RDF datasets. It uses Sparqlify as a SPARQL-to-SQL rewritter for translating SPARQL queries into Spark executable code. Our preliminary results demonstrate that our approach is more extensible, efficient, and scalable as compared to state-of-the-art approaches. Sparklify is integrated into a larger SANSA framework and it serves as a default query engine and has been used by at least three external use scenarios.Property: http://purl.org/dc/elements/1.1/creatorhttps://w3id.org/scholarlydata/person/claus-stadlerhttps://w3id.org/scholarlydata/person/gezim-sejdiuhttps://w3id.org/scholarlydata/person/damien-grauxhttps://w3id.org/scholarlydata/person/jens-lehmannProperty: http://purl.org/dc/elements/1.1/subjectBig Data RDF datasets Scalability Efficient SPARQL SPARQL-to-SQL Query evaluation Apache Spark SANSAProperty: http://purl.org/dc/elements/1.1/titleSparklify: A Scalable Software Component for Efficient evaluation of SPARQL queries over distributed RDF datasetsProperty: http://purl.org/ontology/bibo/authorListhttps://w3id.org/scholarlydata/authorlist/iswc-2019-resource-228Property: http://xmlns.com/foaf/0.1/makerhttps://w3id.org/scholarlydata/person/jens-lehmannhttps://w3id.org/scholarlydata/person/claus-stadlerhttps://w3id.org/scholarlydata/person/damien-grauxhttps://w3id.org/scholarlydata/person/gezim-sejdiuProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#abstractOne of the key traits of Big Data is its complexity in terms of representation, structure, or formats. One existing way to deal with it is offered by Semantic Web standards. Among them, RDF --which proposes to model data with triples representing edges in a graph-- has received a large success and the semantically annotated data has grown steadily towards a massive scale. Therefore, there is a need for scalable and efficient query engines capable of retrieving such information. In this paper, we propose emph{Sparklify}: a scalable software component for efficient evaluation of SPARQL queries over distributed RDF datasets. It uses Sparqlify as a SPARQL-to-SQL rewritter for translating SPARQL queries into Spark executable code. Our preliminary results demonstrate that our approach is more extensible, efficient, and scalable as compared to state-of-the-art approaches. Sparklify is integrated into a larger SANSA framework and it serves as a default query engine and has been used by at least three external use scenarios.Property: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#hasAuthorListhttps://w3id.org/scholarlydata/authorlist/iswc-2019-resource-228Property: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#isPartOfhttps://w3id.org/scholarlydata/conference/iswc/2019/proceedingsProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#keywordBig Data RDF datasets Scalability Efficient SPARQL SPARQL-to-SQL Query evaluation Apache Spark SANSAProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#titleSparklify: A Scalable Software Component for Efficient evaluation of SPARQL queries over distributed RDF datasetsProperty: https://w3id.org/scholarlydata/ontology/conference-ontology.owl#relatesToEventhttps://w3id.org/scholarlydata/talk/iswc-2019-resource-228
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