On this website, you can find information on benchmark specifications and results, on how the LDBC organization works, the motivation for LDBC, and benchmarking resources for the graph and RDF developer community.
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LDBC brings industry and users together for developing benchmarks whereby the state-of-the-art and advances in graph database technologies can be assessed and directed. Read More to find out how the LDBC might help you.
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Here you may find the results for different benchmarks, i.e. the Social Network Benchmark (SNB) and the Semantic Publishing Benchmark (SPB), their definitions and best practices, the repositories where to find the data generators and the query implementations, an access to the intranet for the LDBC industry partners and a list of the LDBC member vendors.
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On these pages, we provide information on the LDBC organization and it members, inclusive information on how to become a member.
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The 5th LDBC Technical User Community (TUC) meeting took place in Athens on 14.11.2014 being well attended by both Graph and RDF databases industry and academia. In the morning session, members of the LDBC project gave an update on the status of the project and its benchmarks:
Up until now we have introduced the challenges faced when executing the LDBC SNB benchmark, as well as explained how some of these are overcome. With the foundations laid, we can now explain precisely how operations are executed.
The SNB Driver part 1 post introduced, broadly, the challenges faced when developing a workload driver for the LDBC SNB benchmark. In this blog we'll drill down deeper into the details of what it means to execute "dependent queries" during benchmark execution, and how this is handled in the driver. First of all, as many driver-specific terms will be used, below is a listing of their definitions. There is no need to read them in detail, it is just there to serve as a point of reference.
Until now we have discussed several aspects of the Semantic Publishing Benchmark (SPB) such as the difference in performance between virtual and real servers configuration
The Semantic Publishing Instance Matching Benchmark (SPIMBench) is a novel benchmark for the assessment of instance matching techniques for RDF data with an associated schema. SPIMBench extends the state-of-the art instance matching benchmarks for RDF data in three main aspects: it allows for systematic scalability testing, supports a wider range of test cases including semantics-aware ones, and provides an enriched gold standard.
We are presently working on the SNB BI workload. Andrey Gubichev of TU Munchen and myself are going through the queries and are playing with two SQL based implementations, one on Virtuoso and the other on Hyper.
As discussed before, the BI workload has the same choke points as TPC-H as a base but pushes further in terms of graphiness and query complexity.
LDBC’s Semantic Publishing Benchmark (SPB) measures the performance of an RDF database in a load typical for metadata-based content publishing, such as the famous BBC Dynamic Semantic Publishing scenario. Such load combines tens of updates per second (e.g.
In previous posts (Getting started with SNB, DATAGEN: data generation for the Social Network Benchmark), Arnau Prat discussed the main features and characteristics of DATAGEN: realism, scalability, determinism, usability. DATAGEN is the social network data generator used by the three LDBC-SNB workloads, which produces data simulating the activity in a social network site duri
In this multi-part blog we consider the challenge of running the LDBC Social Network Interactive Benchmark (LDBC SNB) workload in parallel, i.e.
LDBC SPB (Semantic Publishing Benchmark) is based on the BBC linked data platform use case. Thus the data modelling and transaction mix reflects the BBC's actual utilization of RDF. But a benchmark is not only a condensation of current best practices. The BBC linked data platform is an Ontotext Graph DB deployment. Graph DB was formerly known as Owlim.
