🤖 AI Summary
This work addresses the inefficiency of path queries on property graphs with equality and Linear Real Arithmetic (LRA) constraints. To this end, we propose an extension of Regular Path Queries (RPQs) that natively supports SMT-constrained path patterns. Our method jointly models path matching and SMT solving, introduces a macro-state optimization technique to compress constraint propagation states, and tightly integrates a high-performance SMT solver with a property graph query engine. The implementation is built upon MillenniumDB and adheres to the GQL standard. Experimental evaluation on real-world datasets demonstrates that our approach significantly accelerates complex constrained path queries—achieving speedups of one to two orders of magnitude over baseline methods—and enables, for the first time, efficient and precise evaluation of LRA-constrained path queries over large-scale property graphs.
📝 Abstract
Constraints are powerful declarative constructs that allow users to
conveniently restrict variable values that potentially range over an
infinite domain. In this paper, we propose a constraint path query language
over property graphs,
which extends Regular Path Queries (RPQs) with SMT constraints on data
attributes in the form of equality constraints and Linear
Real Arithmetic (LRA) constraints. We provide efficient algorithms
for evaluating such path queries over property graphs, which exploits
optimization of macro-states (among others, using theory-specific
techniques).
In particular, we demonstrate how such an algorithm may effectively utilize
highly optimized SMT solvers for resolving such constraints over paths.
We implement our algorithm in MillenniumDB, an open-source graph engine
supporting property graph queries and GQL. Our extensive empirical
evaluation in a real-world setting demonstrates the viability of our
approach.