4 research outputs found
The Linked Data Benchmark Council (LDBC): Driving competition and collaboration in the graph data management space
Graph data management is instrumental for several use cases such as
recommendation, root cause analysis, financial fraud detection, and enterprise
knowledge representation. Efficiently supporting these use cases yields a
number of unique requirements, including the need for a concise query language
and graph-aware query optimization techniques. The goal of the Linked Data
Benchmark Council (LDBC) is to design a set of standard benchmarks that capture
representative categories of graph data management problems, making the
performance of systems comparable and facilitating competition among vendors.
LDBC also conducts research on graph schemas and graph query languages. This
paper introduces the LDBC organization and its work over the last decade
The Linked Data Benchmark Council (LDBC): Driving competition and collaboration in the graph data management space
Graph data management is instrumental for several use cases
such as recommendation, root cause analysis, financial fraud detection,
and enterprise knowledge representation. Efficiently supporting these use
cases yields a number of unique requirements, including the need for a
concise query language and graph-aware query optimization techniques.
The goal of the Linked Data Benchmark Council (LDBC) is to design
a set of standard benchmarks that capture representative categories of
graph data management problems, making the performance of systems
comparable and facilitating competition among vendors. LDBC also
conducts research on graph schemas and graph query languages. This
paper introduces the LDBC organization and its work over the last decade