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cloud.google.com·6h ago
AI Research
arXiv CS.AI·27d ago·5 min read
Cost Characterization of Vertically Partitioned Federated Knowledge Graphs
TL;DR
This study evaluates four vertical partitioning strategies for federated knowledge graphs, revealing that cross-silo path length and load balance are key trade-offs. The findings provide actionable insights for optimizing query performance across distributed data silos.
✦ Why It Matters
Evaluate your current partitioning strategy to optimize query performance in federated knowledge graphs.
Key Takeaways
- Assess the impact of partitioning on query latency in your systems.
- Consider locality versus load balance when designing data silos.
- Experiment with different partitioning strategies to find optimal configurations.
- Use the provided metrics to evaluate your current knowledge graph setup.
How It Works
The study formalizes vertical partitioning by analyzing how different strategies affect communication costs and query performance. By isolating the effects of partitioning from the learning model, it identifies key trade-offs that influence the efficiency of multi-hop queries across distributed data silos.