MICRO: A Lightweight Middleware for Optimizing Cross-store Cross-model Graph-Relation Joins [Technical Report]

2026-03-14

AI Summary

This work addresses the inefficiency of cross-model join queries between graph and relational databases in heterogeneous environments by proposing a unified algebraic framework to formally express graph-relational queries. It introduces MICRO, a lightweight middleware that enables native federated execution without data transformation or materialization. The key contributions include the first unified algebra for graph-relational cross-model querying, CMLero—an optimizer based on learning-to-rank that efficiently selects execution plans without requiring precise cost estimation—and a comprehensive multi-benchmark query suite. Experimental results demonstrate that MICRO achieves up to 2.1× speedup on real-world and semi-synthetic workloads; among 93 real queries, 14 exhibit over 100× acceleration (with 4 exceeding 100×), and CMLero significantly outperforms both rule-based and regression-based optimizers.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Search and Optimization: Distributed Search
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Modern data applications increasingly involve heterogeneous data managed in different models and stored across disparate database engines, often deployed as separate installs. Limited research has addressed cross-model query processing in federated environments. This paper takes a step toward bridging this gap by: (1) formally defining a class of cross-model join queries between a graph store and a relational store by proposing a unified algebra; (2) introducing one real-world benchmark and four semi-synthetic benchmarks to evaluate such queries; and (3) proposing a lightweight middleware, MICRO, for efficient query execution. At the core of MICRO is CMLero, a learning-to-rank-based query optimizer that selects efficient execution plans without requiring exact cost estimation. By avoiding the need to materialize or convert all data into a single model, which is often infeasible due to third-party data control or cost, MICRO enables native querying across heterogeneous systems. Experimental results on the benchmark workloads demonstrate that MICRO outperforms the state-of-the-art federated relational system XDB by up to 2.1x in total runtime across the full test set. On the 93 test queries of real-world benchmark, 14 queries achieve over 100 speedup, including 4 queries with more than 100x speedup; however, 4 queries experienced slowdowns of over 5 seconds, highlighting opportunities for future improvement of MICRO. Further comparisons show that CMLero consistently outperforms rule-based and regression-based optimizers, highlighting the advantage of learning-to-rank in complex cross-model optimization.

Problem

• cross-model query
• graph-relational join
• federated database
• heterogeneous data
• query optimization

Innovation

• cross-model query
• graph-relational join
• learning-to-rank optimizer
• federated database middleware
• heterogeneous data integration