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 …
We are delighted to announce the set up of the Financial Benchmark (FinBench) task force.
The Financial Benchmark (FinBench) project aims to define a graph database evaluating
benchmark and develop a data generation process and a query driver to make the evaluation
of the graph database representative, reliable and comparable, especially in financial
scenarios, such as anti-fraud and risk control. The FinBench is scheduled to be released in 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 management. …
LDBC is proud to announce the new LDBC Graphalytics Benchmark draft
specification.
LDBC Graphalytics is the first industry-grade graph data management
benchmark …
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 …