In a previous blog post titled “Is SNB like Facebook's LinkBench?”, Peter Boncz discusses the design philosophy that shapes SNB and how it compares to other existing benchmarks such as LinkBench. In this post, I will briefly introduce the essential parts forming SNB, which are DATAGEN, the LDBC execution driver and the workloads.
During the past six months we (the OWLIM Team at Ontotext) have integrated the LDBC Semantic Publishing Benchmark (LDBC-SPB) as a part of our development and release process.
First thing we’ve started using the LDBC-SPB for is to monitor the performance of our RDF Store when a new release is about to come out.
Initially we’ve decided to fix some of the benchmark parameters :
As explained in a previous post, the LDBC Social Network Benchmark (LDBC-SNB) has the objective to provide a realistic yet challenging workload, consisting of a social network and a set of queries. Both have to be realistic, easy to understand and easy to generate. This post has the objective to discuss the main features of DATAGEN, the social network data generator provided by LDBC-SNB, which is an evolution of S3G21.
The Semantic Publishing Benchmark (SPB), developed in the context of LDBC, aims at measuring the read and write operations that can be performed in the context of a media organisation. It simulates the management and consumption of RDF metadata describing media assets and creative works. The scenario is based around a media organisation that maintains RDF descriptions of its catalogue of creative works. These descriptions use a set of ontologies proposed by BBC that define numerous properties for content; they contain asll RDFS schema constructs and certain OWL ones.
In this post, I will discuss in some detail the rationale and goals of the design of the Social Network Benchmark (SNB) and explain how it relates to real social network data as in Facebook, and in particular FaceBook's own graph benchmark called LinkBench. We think SNB is the most intricate graph database benchmark to date (it's also available in RDF!), that already has made some waves. SNB recently receiv
Social Network interaction is amongst the most natural and widely spread activities in the internet society, and it has turned out to be a very useful way for people to socialise at different levels (friendship, professional, hobby, etc.). As such, Social Networks are well understood from the point of view of the data involved and the interaction required by their actors.
LDBC is proud to announce that Andrey Gubichev and his colleagues of TUM won the graph analytics-targeted SIGMOD 2014 programming contest. In the presentation to the plenary SIGMOD/PODS room, the LDBC Social Network Benchmark data generator, which was used in this contest, was explicitly named, thanked and praised.
The Linked Data Benchmark Council (LDBC) is reaching a milestone today, June 23 2014, in announcing that two of the benchmarks that it has been developing since 1.5 years have now reached the status of Public Draft. This concerns the Semantic Publishing Benchmark (SPB) and the interactive workload of the Social Network Benchmark (SNB). In case of LDBC, the release is staged: now the benchmark software just runs read-only queries. This will be expanded in a few weeks with a mix of read- and insert-queries. Also, query validation will be added later.
The focus of this new workshop is to elicit discussion and description of application areas and open challenges in managing large-scale graph-shaped data.
Experiences including use-case descriptions, war stories and benchmarks as well as system descriptions are important ingredients of the meeting.
The workshop is co-chaired by Josep Larriba Pey (UPC) and Peter Boncz (CWI).