Transformations for Evolving Property Graph Schemas

2026-09-28

AI Summary

This study addresses the lack of systematic support for property graph schema evolution, where manual transformations are difficult to reuse and tightly coupled. To overcome these limitations, this work proposes GRAFT, a framework that models schema evolution as the exploration of reusable meta-transformation sequences composed of atomic edits. Methodologically, it introduces finite element graph search, similarity-guided pruning, and sequential constraints to ensure non-redundancy and termination, while integrating logical reasoning, graph traversal, and greedy strategies for efficient resolution. Experimental evaluations demonstrate that GRAFT efficiently generates high-quality transformation sequences in both benchmark and real-world scenarios, precisely achieving target schemas in most cases.

Abstract

Property graph databases are widely used to represent complex and evolving data; yet, systematic support for property graph schema evolution remains limited. In practice, schema transformations are typically defined manually, coupled to specific application contexts, and are difficult to reuse across schemas or evolution scenarios. We present GRAFT, a logic-based framework that models prop- erty graph schema evolution as reusable, order-constrained meta- transformations derived from atomic edits. Schema evolution is formulated as exploration of a finite meta-graph with schemas as nodes and grounded meta-transformations as edges. To ensure tractability, GRAFT combines similarity-guided search and pruning, guaranteeing duplication-freeness, termination and correctness. An experimental evaluation on four benchmark and real-world property graph schema evolution scenarios shows that GRAFT effi- ciently computes high-quality schema transformation sequences. Using greedy exploration, GRAFT reaches the exact target schema on most datasets, producing stable transformation sequences while keeping runtimes low. A qualitative study on both real-world and a synthetic large-scale dataset further shows the quality and robust- ness of the obtained reusable meta-transformations.

Problem

• Property Graph
• Schema Evolution
• Schema Transformation
• Reusability

Innovation

• Property Graph Schema Evolution
• Meta-transformations
• Logic-based Framework
• Similarity-guided Search
• Meta-graph Exploration