Matches in Ruben’s data for { <https://rdfostrich.github.io/article-jws2018-ostrich/> ?p ?o }
- article-jws2018-ostrich author me.
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- article-jws2018-ostrich about archiving.
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- article-jws2018-ostrich author me.
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- article-jws2018-ostrich title "Triple Storage for Random-Access Versioned Querying of RDF Archives".
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- article-jws2018-ostrich name "Triple Storage for Random-Access Versioned Querying of RDF Archives".
- article-jws2018-ostrich label "Triple Storage for Random-Access Versioned Querying of RDF Archives".
- article-jws2018-ostrich name "Triple Storage for Random-Access Versioned Querying of RDF Archives".
- article-jws2018-ostrich topic archiving.
- article-jws2018-ostrich topic versioning.
- article-jws2018-ostrich topic Linked_Data_Fragments.
- article-jws2018-ostrich topic publication.
- article-jws2018-ostrich topic Triple_Pattern_Fragments.
- article-jws2018-ostrich topic SPARQL.
- article-jws2018-ostrich topic Metadata.
- article-jws2018-ostrich topic Linked_Data.
- article-jws2018-ostrich topic RDF.
- article-jws2018-ostrich topic World_Wide_Web.
- article-jws2018-ostrich subject archiving.
- article-jws2018-ostrich subject versioning.
- article-jws2018-ostrich subject Linked_Data_Fragments.
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- article-jws2018-ostrich subject Triple_Pattern_Fragments.
- article-jws2018-ostrich subject SPARQL.
- article-jws2018-ostrich subject Metadata.
- article-jws2018-ostrich subject Linked_Data.
- article-jws2018-ostrich subject RDF.
- article-jws2018-ostrich subject World_Wide_Web.
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- article-jws2018-ostrich topic archiving.
- article-jws2018-ostrich topic versioning.
- article-jws2018-ostrich topic Linked_Data_Fragments.
- article-jws2018-ostrich topic publication.
- article-jws2018-ostrich topic Triple_Pattern_Fragments.
- article-jws2018-ostrich topic SPARQL.
- article-jws2018-ostrich topic Metadata.
- article-jws2018-ostrich topic Linked_Data.
- article-jws2018-ostrich topic RDF.
- article-jws2018-ostrich topic World_Wide_Web.
- article-jws2018-ostrich abstract "When publishing Linked Open Datasets on the Web, most attention is typically directed to their latest version. Nevertheless, useful information is present in or between previous versions. In order to exploit this historical information in dataset analysis, we can maintain history in RDF archives. Existing approaches either require much storage space, or they expose an insufficiently expressive or efficient interface with respect to querying demands. In this article, we introduce an RDF archive indexing technique that is able to store datasets with a low storage overhead, by compressing consecutive versions and adding metadata for reducing lookup times. We introduce algorithms based on this technique for efficiently evaluating queries at a certain version, between any two versions, and for versions. 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. OSTRICH performs better for many smaller dataset versions than for few larger dataset versions. Furthermore, it enables efficient offsets in query result streams, which facilitates random access in results. Our storage technique reduces query evaluation time for versioned queries 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.".
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