Title
The LDBC Social Network Benchmark: Interactive Workload
Abstract
The Linked Data Benchmark Council (LDBC) is now two years underway and has gathered strong industrial participation for its mission to establish benchmarks, and benchmarking practices for evaluating graph data management systems. The LDBC introduced a new choke-point driven methodology for developing benchmark workloads, which combines user input with input from expert systems architects, which we outline. This paper describes the LDBC Social Network Benchmark (SNB), and presents database benchmarking innovation in terms of graph query functionality tested, correlated graph generation techniques, as well as a scalable benchmark driver on a workload with complex graph dependencies. SNB has three query workloads under development: Interactive, Business Intelligence, and Graph Algorithms. We describe the SNB Interactive Workload in detail and illustrate the workload with some early results, as well as the goals for the two other workloads.
Year
DOI
Venue
2015
10.1145/2723372.2742786
ACM SIGMOD Conference
Field
DocType
Citations 
Data mining,Graph database,Computer science,Workload,Expert system,Linked data,Business intelligence,Data management,Database,Benchmarking,Scalability
Conference
65
PageRank 
References 
Authors
2.28
8
8
Name
Order
Citations
PageRank
Orri Erling148932.75
Alex Averbuch2733.42
Josep-Lluis Larriba-Pey3652.28
Hassan Chafi4111861.11
Andrey Gubichev526311.88
Arnau Prat-Pérez622713.44
Minh-Duc Pham722010.55
Peter Boncz82517244.81