Ruben Verborgh’s data

Matches in Ruben’s data for { <https://comunica.github.io/Article-SSWS2020-AMF/> ?p ?o }

• Article-SSWS2020-AMF author me.
• Article-SSWS2020-AMF author joachim_van_herwegen.
• Article-SSWS2020-AMF author miel_vander_sande.
• Article-SSWS2020-AMF author me.
• Article-SSWS2020-AMF creator me.
• Article-SSWS2020-AMF creator joachim_van_herwegen.
• Article-SSWS2020-AMF creator miel_vander_sande.
• Article-SSWS2020-AMF creator me.
• Article-SSWS2020-AMF about publication.
• Article-SSWS2020-AMF about Triple_Pattern_Fragments.
• Article-SSWS2020-AMF about SPARQL.
• Article-SSWS2020-AMF about Metadata.
• Article-SSWS2020-AMF about Linked_Data.
• Article-SSWS2020-AMF author me.
• Article-SSWS2020-AMF author joachim_van_herwegen.
• Article-SSWS2020-AMF author miel_vander_sande.
• Article-SSWS2020-AMF author me.
• Article-SSWS2020-AMF coparticipatesWith me.
• Article-SSWS2020-AMF coparticipatesWith joachim_van_herwegen.
• Article-SSWS2020-AMF coparticipatesWith miel_vander_sande.
• Article-SSWS2020-AMF coparticipatesWith me.
• Article-SSWS2020-AMF type PublicationVolume.
• Article-SSWS2020-AMF type ScholarlyArticle.
• Article-SSWS2020-AMF type Article.
• Article-SSWS2020-AMF type Document.
• Article-SSWS2020-AMF type Document.
• Article-SSWS2020-AMF type Document.
• Article-SSWS2020-AMF type Q386724.
• Article-SSWS2020-AMF type CreativeWork.
• Article-SSWS2020-AMF type Work.
• Article-SSWS2020-AMF P50 me.
• Article-SSWS2020-AMF P50 joachim_van_herwegen.
• Article-SSWS2020-AMF P50 miel_vander_sande.
• Article-SSWS2020-AMF P50 me.
• Article-SSWS2020-AMF maker me.
• Article-SSWS2020-AMF maker joachim_van_herwegen.
• Article-SSWS2020-AMF maker miel_vander_sande.
• Article-SSWS2020-AMF maker me.
• Article-SSWS2020-AMF title "Optimizing Approximate Membership Metadata in Triple Pattern Fragments for Clients and Servers".
• Article-SSWS2020-AMF isPartOf proceedings_of_the_13th_international_workshop_on_scalable_semantic_web_knowledge_base_systems.
• Article-SSWS2020-AMF name "Optimizing Approximate Membership Metadata in Triple Pattern Fragments for Clients and Servers".
• Article-SSWS2020-AMF label "Optimizing Approximate Membership Metadata in Triple Pattern Fragments for Clients and Servers".
• Article-SSWS2020-AMF name "Optimizing Approximate Membership Metadata in Triple Pattern Fragments for Clients and Servers".
• Article-SSWS2020-AMF topic publication.
• Article-SSWS2020-AMF topic Triple_Pattern_Fragments.
• Article-SSWS2020-AMF topic SPARQL.
• Article-SSWS2020-AMF topic Metadata.
• Article-SSWS2020-AMF topic Linked_Data.
• Article-SSWS2020-AMF subject publication.
• Article-SSWS2020-AMF subject Triple_Pattern_Fragments.
• Article-SSWS2020-AMF subject SPARQL.
• Article-SSWS2020-AMF subject Metadata.
• Article-SSWS2020-AMF subject Linked_Data.
• Article-SSWS2020-AMF authorList b0_b1835.
• Article-SSWS2020-AMF topic publication.
• Article-SSWS2020-AMF topic Triple_Pattern_Fragments.
• Article-SSWS2020-AMF topic SPARQL.
• Article-SSWS2020-AMF topic Metadata.
• Article-SSWS2020-AMF topic Linked_Data.
• Article-SSWS2020-AMF abstract "Depending on the HTTP interface used for publishing Linked Data, the effort of evaluating a SPARQL query can be redistributed differently between clients and servers. For instance, lower server-side CPU usage can be realized at the expense of higher bandwidth consumption. Previous work has shown that complementing lightweight interfaces such as Triple Pattern Fragments (TPF) with additional metadata can positively impact the performance of clients and servers. Specifically, Approximate Membership Filters (AMFs)—data structures that are small and probabilistic—in the context of TPF were shown to reduce the number of HTTP requests, at the expense of increasing query execution times. In order to mitigate this significant drawback, we have investigated unexplored aspects of AMFs as metadata on TPF interfaces. In this article, we introduce and evaluate alternative approaches for server-side publication and client-side consumption of AMFs within TPF to achieve faster query execution, while maintaining low server-side effort. Our alternative client-side algorithm and the proposed server configurations significantly reduce both the number of HTTP requests and query execution time, with only a small increase in server load, thereby mitigating the major bottleneck of AMFs within TPF. Compared to regular TPF, average query execution is more than 2 times faster and requires only 10% of the number of HTTP requests, at the cost of at most a 10% increase in server load. These findings translate into a set of concrete guidelines for data publishers on how to configure AMF metadata on their servers.".
• Article-SSWS2020-AMF datePublished "2020".
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• Article-SSWS2020-AMF number "2757".
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