TriORM: Workload-Aware Neural--Symbolic Multi-Objective Optimization for ORM Mapping Design

Sasan Azizian, Ayoub Hazrati, Artin Azizian, Elham Rastegari

Keywords: Object--Relational Mapping, Performance Prediction, Neural--Symbolic Systems, Multi-objective Optimization, Schema Synthesis, Regression

TL;DR: TriORM combines symbolic synthesis with workload-conditioned learning to rank only valid ORM mappings by predicted query, write, and storage tradeoffs, avoiding per-candidate benchmarking in the recommendation loop.

Abstract: Object-relational mapping (ORM) frameworks simplify persistence, yet routine mapping choices---including inheritance strategy, association encoding, and denormalization---can substantially alter the generated SQL and the trade-offs among query latency, insert/update cost, and storage footprint. Existing optimization approaches either guarantee semantic validity but incur expensive per-candidate deployment and benchmarking, or learn from schema structure alone and therefore miss the fact that workload behavior is mapping-dependent. We present \textsc{TriORM}, a workload-aware neural--symbolic framework for multi-objective ORM mapping design that removes per-candidate workload execution from the \emph{online} recommendation loop while preserving validity by construction. \textsc{TriORM} (i) enumerates admissible mappings via bounded relational synthesis in Alloy, (ii) concretizes an abstract workload into schema-specific SQL templates for each candidate, and (iii) predicts continuous objectives using a tri-input model that fuses a typed schema-graph encoder, a concretized-workload encoder, and interpretable static cost proxies. The resulting predictions enable Pareto filtering and user-weighted selection without deploying or executing each candidate at inference time; profiling is performed only \emph{offline} to obtain supervision. On nine TradeMaker/\textsc{Leant} benchmark models, \textsc{TriORM} improves mean Pareto-front approximation over \textsc{Leant} (GD/HV $0.03/0.78$ vs.\ $0.07/0.61$) and reduces end-to-end recommendation time ($3.6{\times}10^3$s vs.\ $2.6{\times}10^4$s), while preserving semantic correctness within the chosen synthesis bounds.

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Paper Type: Full-length papers (i.e. case studies, theoretical, applied research papers). 8 pages

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Submission Number: 4