2023 has been an eventful year for us so far. Here is a summary of our recent activities.
Our paper The LDBC Social Network Benchmark: Business Intelligence Workload was published in PVLDB.
David Püroja just completed his MSc thesis on creating a design towards SNB Interactive v2 at CWI’s Database Architectures group. David and I gave a deep-dive talk at the FOSDEM conference’s graph developer room titled The LDBC Social Network …
LDBC SNB provides a data generator, which produces synthetic datasets, mimicking a social network’s activity during a period of time. Datagen is defined by the charasteristics of realism, scalability, determinism and usability. More than two years have elapsed since my last technical update on LDBC SNB Datagen, in which I discussed the reasons for moving the code to Apache Spark from the MapReduce-based Apache Hadoop implementation and the …
LDBC’s Social Network Benchmark [4] (LDBC SNB) is an industrial and academic initiative, formed by principal actors in the field of graph-like data management. Its goal is to define a framework where different graph-based technologies can be fairly tested and compared, that can drive the identification of systems’ bottlenecks and required functionalities, and can help researchers open new frontiers in high-performance graph data …
Apache Flink [1] is an open source platform for distributed stream and batch data processing. Flink’s core is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations over data streams. Flink also builds batch processing on top of the streaming engine, overlaying native iteration support, managed memory, and program optimization.
Flink offers multiple APIs to process data …
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 …
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), …
This blog entry is about one of the features of DATAGEN that makes it different from other synthetic graph generators that can be found in the literature: the community structure of the graph.
When generating synthetic graphs, one must not only pay attention to quantitative measures such as the number of nodes and edges, but also to other more qualitative characteristics such as the degree distribution, clustering coefficient. Real graphs, and …
When talking about DATAGEN and other graph generators with social network characteristics, our attention is typically borrowed by the friendship subgraph and/or its structure. However, a social graph is more than a bunch of people being connected by friendship relations, but has a lot more of other things is worth to look at. With a quick view to commercial social networks like Facebook, Twitter or Google+, one can easily identify a lot of other …
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. Thus, the concepts of friends of friends, or retweet are well established …
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 S3G2 [1].
One of the most …