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An early look at the LDBC Social Network Benchmark's Business Intelligence workload

Szárnyas, Gábor and Prat-Pérez, Arnau and Averbuch, Alex and Marton, József and Paradies, Marcus and Boncz, Péter and Antal, János Benjámin (2018) An early look at the LDBC Social Network Benchmark's Business Intelligence workload. In: GRADES-NDA '18: Proceedings of the 1st ACM SIGMOD Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA). Association for Computing Machinery, New York, pp. 1-11. ISBN 978-1-4503-5695-4

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Abstract

In this short paper, we provide an early look at the LDBC Social Network Benchmark's 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.

Item Type: Book Section
Subjects: Q Science / természettudomány > QA Mathematics / matematika > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
SWORD Depositor: MTMT SWORD
Depositing User: MTMT SWORD
Date Deposited: 04 Jul 2018 06:52
Last Modified: 04 Jul 2018 06:52
URI: http://real.mtak.hu/id/eprint/81011

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