The 5th LDBC TUC meeting will take place in Athens on 14.11.2014. We welcome RDF and graph database users to explain their use-cases, describe the limitations they have found in current technology and see the progress of the LDBC benchmarks i.e. the Semantic Publishing Benchmark and the Social Network Benchmark. The agenda will be available soon at http://wiki.ldbcouncil.org/display/TUC/Fifth+TUC+Meeting%2C+Athens%2C+Greece%2C+November+14
Note: consider this post as a continuation of the "Making it interactive" post by Orri Erling.
I have now completed the Virtuoso TPC-H work, including scale out. Optimization possibilities extend to infinity but the present level is good enough. TPC-H is the classic of all analytics benchmarks and is difficult enough, I have extensive commentary on this on my blog (In Hoc Signo Vinces series), including experimental results. This is, as it were, the cornerstone of the true science. This is however not the totality of it. From the LDBC angle, we might liken this to the last camp before attempting a mountain peak.
Synopsis: Now is the time to finalize the interactive part of the Social Network Benchmark (SNB). The benchmark must be both credible in a real social network setting and pose new challenges. There are many hard queries but not enough representation for what online systems in fact do. So, the workload mix must strike a balance between the practice and presenting new challenges.
In previous posts (this and this) we briefly introduced the design goals and philosophy behind DATAGEN, the data generator used in LDBC-SNB. In this post, I will explain how to use DATAGEN to generate the necessary datatsets to run LDBC-SNB. Of course, as DATAGEN is continuously under development, the instructions given in this tutorial might change in the future.
The LDBC Social Network Benchmark (SNB) is composed of three distinct workloads, interactive, business intelligence and graph analytics. This post introduces the interactive workload.
The benchmark measures the speed of queries of medium complexity against a social network being constantly updated. The queries are scoped to a user's social environment and potentially access data associated with the friends or a user and their friends.
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, 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