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Conference Papers Year : 2017

gMark: Schema-Driven Generation of Graphs and Queries


Massive graph data sets are pervasive in contemporary application domains. Hence, graph database systems are becoming increasingly important. In the experimental study of these systems, it is vital that the research community has shared solutions for the generation of database instances and query workloads having predictable and controllable properties. We present the design and engineering principles of gMark, a domain- and query language-independent graph instance and query workload generator. A core contribution of gMark is its ability to target and control the diversity of properties of both the generated instances and the generated workloads coupled to these instances. Further novelties include support for regular path queries, a fundamental graph query paradigm, and schema-driven selectivity estimation of queries, a key feature in controlling workload chokepoints. We illustrate the flexibility and practical usability of gMark by showcasing the framework's capabilities in generating high quality graphs and workloads, and its ability to encode user-defined schemas across a variety of application domains.

Dates and versions

hal-01591706 , version 1 (21-09-2017)



Guillaume Bagan, Angela Bonifati, Radu Ciucanu, George Fletcher, Aurélien Lemay, et al.. gMark: Schema-Driven Generation of Graphs and Queries. Data Engineering (ICDE), 2017 IEEE 33rd International Conference on, Apr 2017, San Diego, United States. pp.63-64, ⟨10.1109/ICDE.2017.38⟩. ⟨hal-01591706⟩
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