™RDF4J 4.2.1 is a patch release that fixes 11 issues.
For a complete overview, see all issues fixed in 4.2.1.
The following vulnerable dependencies have been upgraded:
This release was made possible by contributions from Bart Hanssens, Håvard M. Ottestad, Denitsa Stoianova, Patrick Wyler, and Andreas Schwarte.
RDF4J 4.2.0 is a minor release introducing support for custom SPARQL aggregate functions as well as several performance improvements and bug fixes.
The support for custom SPARQL aggregate functions has allowed us to implement functions for standard deviation and variance using Apache Commons Math3.
| IRI | Description |
|---|---|
| http://rdf4j.org/aggregate#stdev | Standard deviation using sampling. |
| http://rdf4j.org/aggregate#stdev_population | Standard deviation using statistical population. |
| http://rdf4j.org/aggregate#variance | Variance using sampling. |
| http://rdf4j.org/aggregate#variance_population | Variance using statistical population. |
Query example
SELECT (<http://rdf4j.org/aggregate#stdev>(?o) AS ?stdev) WHERE {
?s ?p ?o .
}
More accurate query statistics for the LMDB Store drastically improves query performance. The LMDB Store can already scale to considerably larger datasets than the NativeStore and now also matches the performance for smaller datasets.
RDF4J 4.1.3 is a patch release that fixes 5 issues.
For a complete overview, see all issues fixed in 4.1.3.
This release was made possible by contributions from nguyenm100, Håvard Ottestad, Desislava Hristova, pulquero and Wolfgang Schell.
Eclipse RDF4J™ is a powerful Java framework for processing and handling RDF data. This includes creating, parsing, scalable storage, reasoning and querying with RDF and Linked Data. It offers an easy-to-use API that can be connected to all leading RDF database solutions. It allows you to connect with SPARQL endpoints and create applications that leverage the power of linked data and Semantic Web.
