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AWAPart: Adaptive Workload-Aware Partitioning Knowledge Graphs

Authors:
Amitabh Priyadarshi
Krzysztof J. Kochut

Keywords: knowledge graphs; adaptive graph partition; query workload

Abstract:
Large-scale knowledge graphs are increasingly common in many domains. Their large sizes often exceed the limits of systems storing the graphs in a centralized data store, especially if placed in main memory. To overcome this, large knowledge graphs need to be partitioned into multiple sub-graphs and placed in nodes in a distributed system. But querying these fragmented subgraphs poses new challenges, such as increased communication costs, due to distributed joins involving cut edges. To combat these problems, a good partitioning should reduce the edge cuts while considering a given query workload. However, a partitioned graph needs to be continually re-partitioned to accommodate changes in the query workload and maintain a good average processing time. In this paper, an adaptive partitioning method for large-scale knowledge graphs is introduced, which adapts the partitioning in response to changes in the query workload. Our evaluation demonstrates that the performance of processing time for queries is improved after dynamically adapting the partitioning of knowledge graph triples.

Pages: 12 to 17

Copyright: Copyright (c) IARIA, 2021

Publication date: October 3, 2021

Published in: conference

ISSN: 2308-4510

ISBN: 978-1-61208-888-4

Location: Barcelona, Spain

Dates: from October 3, 2021 to October 7, 2021