AWAPart: Adaptive Workload-Aware Partitioning of Knowledge Graphs
This work addresses the challenge of efficient querying in distributed knowledge graph systems for domains handling large data, but it appears incremental as it builds on existing partitioning techniques with workload adaptation.
The paper tackles the problem of partitioning large-scale knowledge graphs for distributed systems to reduce communication costs from edge cuts, and introduces an adaptive method that improves query processing time by dynamically adjusting partitioning based on workload changes.
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 sub-graphs 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.