Evaluation of Pattern Matching Workloads in Graph Analysis Systems

Abstract

Graph data management and mining became a popular area of research, and led to the development of plethora of systems in recent years. Unfortunately, a number of emerging graph analysis systems assume different graph data models, and support different query interface and serialization formats. Such diversity, combined with a lack of comparisons, makes it complicated to understand the trade-offs between different systems and the graph operations for which they are designed. This study presents an evaluation of graph pattern matching capabilities of six graph analysis systems, by extending the Lehigh University Benchmark to investigate the degree of effectiveness to perform the same operation over the same graph in various graph analysis systems. Through the evaluation, this study reveals both quantitative and qualitative findings.