Graph data management techniques are employed in several domains such as finance and enterprise knowledge representation for evaluating graph pattern matching and path finding queries on large data sets. Supporting such queries efficiently 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 standard benchmarks which capture representative categories of graph data management problems, making the performance of systems comparable and facilitating competition among vendors. This talk describes the Business Intelligence workload, a graph OLAP benchmark with global graph queries that use pattern matching, path finding, and aggregation operations. The workload is executed on a dynamic social network graph updated in daily batches of inserts and deletes. We discuss the design process of the benchmark and present its first stable version.
Gábor Szárnyas is a post-doctoral researcher. He obtained his PhD in software engineering in 2019, focusing on the intersection of object-oriented graph models and property graphs. He currently works on efficient graph processing techniques, including formulating graph algorithms in the language of linear algebra (GraphBLAS), implementing graph query engines (SQL/PGQ), and designing graph benchmarks. He serves on the steering committee of the Linked Data Benchmark Council.