We are pleased to announce the release of GraphDB 10.1, which offers significant performance improvements and lower memory usage, and makes it easier to develop robust solutions with semantic technologies thanks to new SPARQL extensions and search functionality. It also addresses the needs of those who are new to RDF by providing interactive user guides.
Read on to find get acquainted with all the new features in GraphDB 10.1.
We have invested substantial effort in optimizing the GraphDB engine in general. The optimizations are not specific to any benchmark and should benefit all users, especially those with larger datasets or slower hardware. As a result, the query performance was improved by roughly 30% to 50%, depending on the concurrency and hardware used.
The optimization also resulted in lower memory usage, which is particularly noticeable on larger datasets. Typical memory usage is now 15% to 20% less.
As a consequence of all optimizations, GraphDB can now pass the LDBC Semantic Network Benchmark (SNB) in its entirety. This makes GraphDB the only RDF database that can pass the benchmark (pending official audited results). SNB is the most comprehensive benchmark for graph analytics and until now was primarily targeted at labeled property graph (LPG) engines.
An important criterion when deciding whether you should use GraphDB is how easy it is for your developers to accomplish the given tasks and thus minimize the time and cost. GraphDB is built on a solid foundation of standards such as RDF and SPARQL, but in many cases the standards are not enough. We have addressed this issue by developing new SPARQL functions and a simple full-text search index.
We have implemented many additional SPARQL functions that make it much easier to accomplish specific tasks. The new functions are compatible with equivalent functions in Jena so that developers who are used to how things are done with Jena can switch to GraphDB effortlessly.
The new functions for working with RDF lists are worth mentioning as they provide a much-needed mechanism for harnessing the power of RDF lists inside SPARQL.
The new aggregate functions for calculating the standard deviation and variance address the needs of those who perform data analysis with numbers.
The GraphDB connectors provide a powerful facility for full-text search but with power comes complexity – every connector instance needs a predefined data model that is used to construct a document.
On the other hand, many use-cases need a full-text search mechanism that is as easy and transparent as using a substring search with a standard SPARQL function. We have developed a new simple full-text search index for literals and IRIs. The index has high performance and an easy usage syntax compliant with SPARQL 1.1. It supports over 40 languages and requires minimal configuration without the need to describe or follow a document model.
Semantic technology offers a new paradigm for organizing and managing complex enterprise data. While the technology is very powerful, people may be unfamiliar with it. To address the needs of beginners, we have developed a system of interactive user guides that lead the user through the workbench showing them how to accomplish various tasks and get started with RDF.
In future versions, we plan to develop more interactive guides that go beyond the beginner level as we believe they are powerful tools for showcasing interesting features.
As a general strategy to offer a secure and reliable product, we strive to provide up-to-date versions of third-party libraries. This includes both features and bug fixes provided by the libraries and also addresses newly identified public vulnerabilities.
The RDF4J library in GraphDB is now upgraded to 4.2.0 and also brings SHACL improvements and general bug fixes.
For more information, contact Doug Kimball, Chief Marketing Officer at Ontotext