Community-based RDF graph partitioning

Conference item

A common approach to processing large RDF datasets is to partition the data in a cluster of shared-nothing servers and then use a distributed query evaluation algorithm. It is commonly assumed in the literature that the performance of query processing in such systems is limited mainly by network communication. In this paper, we show that this assumption does not always hold: we present a new RDF partitioning method based on Louvain community detection, which drastically reduces communication, but without a corresponding decrease in query running times. We show that strongly connected partitions can incur workload imbalance among the servers during query processing. We thus further refined our technique to strike a balance between reducing communication and spreading processing more evenly, and we show that this technique can reduce both communication and query times.

Authors: Banda, F; Motik, B

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: CEUR Workshop Proceedings
• Host title: SSWS 2020: Scalable Semantic Web Knowledge Base Systems
• Volume: 2757
• Pages: 33-48
• Publication date: 2020-12-02
• Acceptance date: 2020-09-13
• Event title: SSWS 2020: 13th International Workshop on Scalable Semantic Web Knowledge Base Systems
• Event location: Athens, Greece
• Event website: http://www.ssws-ws.org/SSWS2020/index.html
• Event start date: 2020-11-02
• Event end date: 2020-11-02
• ISSN: 1613-0073
• Language: English
• Keywords: Louvain modularity; Community partitioning; FFR; RDF