A Task-Regime Perspective on Zero-Knowledge Database Migration

A Task-Regime Perspective on Zero-Knowledge Database Migration | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Task-Regime Perspective on Zero-Knowledge Database Migration Dr. Kirti Wanjale, Aadi Joshi, Kavya Bhand, Kabir Khanuja This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9205841/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Relational schemas are naturally graph-structured, motivating the widespread use of Graph Neural Networks (GNNs) for database migration tasks such as foreign key discovery, integrity validation, and impact analysis. However, we show that treating all schema learning problems as monolithic graph tasks conflates fundamentally different computational regimes. We introduce a principled taxonomy that decomposes migration tasks into local tasks, solvable from pairwise column features, and relational tasks, requiring multi-hop structural reasoning. Across three datasets - Spider (166 academic databases), SchemaPile (real-world GitHub schemas), and Stack Overflow (enterprise-scale schema) - we conduct 11 controlled experiments comparing MLPs, multiple GNN architectures, DeepSets, SQL baselines, and classical heuristics. We find that: (1) For local tasks such as foreign key discovery, a simple MLP on pairwise column features outperforms GNNs by up to +129% in F1 (p 0.99, while MLPs plateau at R² ≈ 0.73. (3) Under partial-access enterprise workflows (40 - 60% schema hidden), GNNs degrade gracefully (MAE ≈ 1.1), whereas SQL traversal fails catastrophically. We further formalize structural noise injection, proving that message passing reduces signal-to-noise ratio in sparse-FK schemas, explaining observed over-smoothing effects. Based on these findings, we propose a regime-aware hybrid pipeline that assigns inductive bias per task type and strictly dominates monolithic approaches. All experiments are fully reproducible, with code and results publicly released. Database Migration Graph Neural Networks Relational Reasoning Schema Learning Zero-Knowledge Databases Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9205841","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611041059,"identity":"12919c52-34cc-4c4d-8a1d-aa4d384299af","order_by":0,"name":"Dr. Kirti 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However, we show that treating all schema learning problems as monolithic graph tasks conflates fundamentally different computational regimes. We introduce a principled taxonomy that decomposes migration tasks into local tasks, solvable from pairwise column features, and relational tasks, requiring multi-hop structural reasoning. Across three datasets - Spider (166 academic databases), SchemaPile (real-world GitHub schemas), and Stack Overflow (enterprise-scale schema) - we conduct 11 controlled experiments comparing MLPs, multiple GNN architectures, DeepSets, SQL baselines, and classical heuristics. We find that: (1) For local tasks such as foreign key discovery, a simple MLP on pairwise column features outperforms GNNs by up to +129% in F1 (p \u0026lt; 0.001), as message passing injects structural noise from non-FK neighbors. (2) For relational tasks such as propagation count and blast-radius estimation, GNNs are indispensable, achieving R² \u0026gt; 0.99, while MLPs plateau at R² ≈ 0.73. (3) Under partial-access enterprise workflows (40 - 60% schema hidden), GNNs degrade gracefully (MAE ≈ 1.1), whereas SQL traversal fails catastrophically. We further formalize structural noise injection, proving that message passing reduces signal-to-noise ratio in sparse-FK schemas, explaining observed over-smoothing effects. Based on these findings, we propose a regime-aware hybrid pipeline that assigns inductive bias per task type and strictly dominates monolithic approaches. 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