Despite the increasing interest in graph databases their requirements and specifications are not yet fully understoodby everyone, leading to a great deal of variation in the supported functionalities and the achieved performances. Inthis work, we provide a comprehensive study of the existing graph database systems. We introduce a novel microbenchmarking framework that provides insights on their performance that go beyond what macro-benchmarks can offer. The framework includes the largest set of queries andoperators so far considered. The graph database systemsare evaluated on synthetic and real data, from different domains, and at scales much larger than any previous work.The framework is materialized as an open-source suite andis easily extended to new datasets, systems, and queries1.

Beyond Macrobenchmarks: Microbenchmark-based Graph Database Evaluation

Matteo Lissandrini;
2018-01-01

Abstract

Despite the increasing interest in graph databases their requirements and specifications are not yet fully understoodby everyone, leading to a great deal of variation in the supported functionalities and the achieved performances. Inthis work, we provide a comprehensive study of the existing graph database systems. We introduce a novel microbenchmarking framework that provides insights on their performance that go beyond what macro-benchmarks can offer. The framework includes the largest set of queries andoperators so far considered. The graph database systemsare evaluated on synthetic and real data, from different domains, and at scales much larger than any previous work.The framework is materialized as an open-source suite andis easily extended to new datasets, systems, and queries1.
2018
data management, query processing, knowledge graph
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11562/1119500
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