Authors: Bingqing Lyu, Xiaoli Zhou, Longbin Lai, Yufan Yang, Yunkai Lou, Wenyuan Yu, Jingren Zhou

Published on: January 31, 2024

Impact Score: 8.15

Arxiv code: Arxiv:2401.17786

Summary

• What is new: Introducing GOpt, a graph-native query optimization framework with a unified intermediate representation to handle both graph and relational operations efficiently.
• Why this is important: The challenge in optimizing graph queries (PatRelQuery) that mix pattern matching with relational operations due to their complexity and arbitrary type constraints.
• What the research proposes: GOpt employs a unified intermediate representation to streamline query optimization and uses automatic type inference and a set of optimization rules to address type constraints in PatRelQuery.
• Results: GOpt significantly enhances query performance, as shown in experiments with both crafted benchmarks and real-world applications.

Technical Details

Technological frameworks used: GOpt

Models used: Unified intermediate representation (IR), automatic type inference, cost-based optimization techniques.

Data used: Crafted benchmarks and real-world application data

Potential Impact

GOpt’s advancements could benefit companies relying on extensive graph-based data analysis, including social networks, financial services for fraud detection, and recommendation systems.

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