Ruben Verborgh’s data

Matches in Ruben’s data for { <https://rdfostrich.github.io/article-iswc2019-journal-ostrich/> ?p ?o }

• article-iswc2019-journal-ostrich author me.
• article-iswc2019-journal-ostrich author joachim_van_herwegen.
• article-iswc2019-journal-ostrich author erik_mannens.
• article-iswc2019-journal-ostrich author miel_vander_sande.
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• article-iswc2019-journal-ostrich creator me.
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• article-iswc2019-journal-ostrich creator me.
• article-iswc2019-journal-ostrich about archiving.
• article-iswc2019-journal-ostrich about versioning.
• article-iswc2019-journal-ostrich about Linked_Data_Fragments.
• article-iswc2019-journal-ostrich about publication.
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• article-iswc2019-journal-ostrich about RDF.
• article-iswc2019-journal-ostrich about World_Wide_Web.
• article-iswc2019-journal-ostrich author me.
• article-iswc2019-journal-ostrich author joachim_van_herwegen.
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• article-iswc2019-journal-ostrich maker me.
• article-iswc2019-journal-ostrich title "Reflections On: Triple Storage for Random-Access Versioned Querying of RDF Archives".
• article-iswc2019-journal-ostrich isPartOf proceedings_of_the_18th_international_semantic_web_conference_journal_track.
• article-iswc2019-journal-ostrich name "Reflections On: Triple Storage for Random-Access Versioned Querying of RDF Archives".
• article-iswc2019-journal-ostrich label "Reflections On: Triple Storage for Random-Access Versioned Querying of RDF Archives".
• article-iswc2019-journal-ostrich name "Reflections On: Triple Storage for Random-Access Versioned Querying of RDF Archives".
• article-iswc2019-journal-ostrich topic archiving.
• article-iswc2019-journal-ostrich topic versioning.
• article-iswc2019-journal-ostrich topic Linked_Data_Fragments.
• article-iswc2019-journal-ostrich topic publication.
• article-iswc2019-journal-ostrich topic Triple_Pattern_Fragments.
• article-iswc2019-journal-ostrich topic SPARQL.
• article-iswc2019-journal-ostrich topic Metadata.
• article-iswc2019-journal-ostrich topic Linked_Data.
• article-iswc2019-journal-ostrich topic RDF.
• article-iswc2019-journal-ostrich topic World_Wide_Web.
• article-iswc2019-journal-ostrich subject archiving.
• article-iswc2019-journal-ostrich subject versioning.
• article-iswc2019-journal-ostrich subject Linked_Data_Fragments.
• article-iswc2019-journal-ostrich subject publication.
• article-iswc2019-journal-ostrich subject Triple_Pattern_Fragments.
• article-iswc2019-journal-ostrich subject SPARQL.
• article-iswc2019-journal-ostrich subject Metadata.
• article-iswc2019-journal-ostrich subject Linked_Data.
• article-iswc2019-journal-ostrich subject RDF.
• article-iswc2019-journal-ostrich subject World_Wide_Web.
• article-iswc2019-journal-ostrich authorList b0_b1931.
• article-iswc2019-journal-ostrich topic archiving.
• article-iswc2019-journal-ostrich topic versioning.
• article-iswc2019-journal-ostrich topic Linked_Data_Fragments.
• article-iswc2019-journal-ostrich topic publication.
• article-iswc2019-journal-ostrich topic Triple_Pattern_Fragments.
• article-iswc2019-journal-ostrich topic SPARQL.
• article-iswc2019-journal-ostrich topic Metadata.
• article-iswc2019-journal-ostrich topic Linked_Data.
• article-iswc2019-journal-ostrich topic RDF.
• article-iswc2019-journal-ostrich topic World_Wide_Web.
• article-iswc2019-journal-ostrich abstract "In addition to their latest version, Linked Open Datasets on the Web can also contain useful information in or between previous versions. In order to exploit this information, we can maintain history in RDF archives. Existing approaches either require much storage space, or they do not meet sufficiently expressive querying demands. In this extended abstract, we discuss an RDF archive indexing technique that has a low storage overhead, and adds metadata for reducing lookup times. We introduce algorithms based on this technique for efficiently evaluating versioned queries. Using the BEAR RDF archiving benchmark, we evaluate our implementation, called OSTRICH. Results show that OSTRICH introduces a new trade-off regarding storage space, ingestion time, and querying efficiency. By processing and storing more metadata during ingestion time, it significantly lowers the average lookup time for versioning queries. Our storage technique reduces query evaluation time through a preprocessing step during ingestion, which only in some cases increases storage space when compared to other approaches. This allows data owners to store and query multiple versions of their dataset efficiently, lowering the barrier to historical dataset publication and analysis.".
• article-iswc2019-journal-ostrich datePublished "2019".
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• article-iswc2019-journal-ostrich number "2576".
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