Next 31st of May the GRADES workshop will take place in Melbourne within the ACM/SIGMOD presentation. GRADES started as an initiative of the Linked Data Benchmark Council in the SIGMOD/PODS 2013 held in New York.

Among the papers published in this edition we have “Graphalytics: A Big Data Benchmark for Graph-Processing Platforms”, which presents a new benchmark that uses the Social Network Benchmark data generator of LDBC (that can …

​SNB Interactive is the wild frontier, with very few rules. This is necessary, among other reasons, because there is no standard property graph data model, and because the contestants support a broad mix of programming models, ranging from in-process APIs to declarative query.

In the case of Virtuoso, we have played with SQL and SPARQL implementations. For a fixed schema and well known workload, SQL will always win. The reason for this is that …

LDBC is presenting two papers at the next edition of the ACM SIGMOD/PODS conference held in Melbourne from May 31st to June 4th, 2015. The annual ACM SIGMOD/PODS conference is a leading international forum for database researchers, practitioners, developers, and users to explore cutting-edge ideas and results, and to exchange techniques, tools and experiences.

On the industry track, LDBC will be presenting the Social Network Benchmark Interactive …

This post is the first in a series of blogs analyzing the LDBC Social Network Benchmark Interactive workload. This is written from the dual perspective of participating in the benchmark design and of building the OpenLink Virtuoso implementation of same.

With two implementations of SNB interactive at four different scales, we can take a first look at what the benchmark is really about. The hallmark of a benchmark implementation is that its …

In a previous 3-part blog series we touched upon the difficulties of executing the LDBC SNB Interactive (SNB) workload, while achieving good performance and scalability. What we didn’t discuss is why these difficulties were unique to SNB, and what aspects of the way we perform workload execution are scientific contributions - novel solutions to previously unsolved problems. This post will highlight the differences between SNB and more …

As discussed in previous posts, one of the features that makes Datagen more realistic is the fact that the activity volume of the simulated Persons is not uniform, but forms spikes. In this blog entry I want to explain more in depth how this is actually implemented inside of the generator.

First of all, I start with a few basics of how Datagen works internally. In Datagen, once the person graph has been created (persons and their relationships), …