Paper Info
Title
An early look at the LDBC social network benchmark's business intelligence workload.
Abstract
In this short paper, we provide an early look at the LDBC Social Network Benchmarku0027s Business Intelligence (BI) workload which tests graph data management systems on a graph business analytics workload. Its queries involve complex aggregations and navigations (joins) that touch large data volumes, which is typical in BI workloads, yet they depend heavily on graph functionality such as connectivity tests and path finding. We outline the motivation for this new benchmark, which we derived from many interactions with the graph database industry and its users, and situate it in a scenario of social network analysis. The workload was designed by taking into account technical chokepoints identified by database system architects from academia and industry, which we also describe and map to the queries. We present reference implementations in openCypher, PGQL, SPARQL, and SQL, and preliminary results of SNB BI on a number of graph data management systems.
Year
Venue
Field
2018
GRADES/NDA@SIGMOD/PODS
Data science,SQL,Graph database,Business analytics,Computer science,Workload,Social network analysis,SPARQL,Business intelligence,Data management,Database
DocType
Citations 
PageRank 
Conference
1
0.34
References 
Authors
28
10
Name
Order
Citations
PageRank
Gábor Szárnyas1537.84
Arnau Prat-Pérez222713.44
Alex Averbuch3733.42
József Marton411.02
Marcus Paradies58210.36
Moritz Kaufmann6232.53
Orri Erling748932.75
Peter Boncz82517244.81
Vlad Haprian910.34
János Benjamin Antal1011.02