[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82764-en":3,"doc-seo-82764-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},82764,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","A Fair Benchmarking of Deep Relational Database Learning Models","Relational databases (RDBs) serve as core enterprise data infrastructure, yet deep learning methods targeting RDBs have been compared under inconsistent experimental protocols, hindering reliable conclusions. This study presents a systematic benchmarking of recent deep RDB learning approaches across five relational databases with one classification and one regression task per dataset. Models are refactored to enable consistent experimentation. Results show relational transformers (RT) achieve the strongest overall performance on both task types.","A Fair Benchmarking of Deep Relational Database Learning  \nModels  \narXiv :2607 .03659v 1 [ cs .DB] 4 Jul 2026  \nKazi F. Akhter, Manar D. Samad  \nDepartment of Computer Science Tennessee State University Nashville, TN, USA[manar.samad@outlook.com](manar.samad@outlook.com)  \nBharath Ajendla  \nSAP AI Research and Innovation SAP Labs Palo Alto, CA, USA  \nJuly 7, 2026  \nABSTRACT  \nRelational databases (RDBs) are the primary data infrastructure in many enterprises, yet recent deep learning methods designed for RDBs have been evaluated under inconsistent experimental protocols, making fair comparison difficult. We present one of the first systematic benchmarking studies of recently released deep learning methods for RDBs, evaluating them across five relational databases, with one classification and one regression task for each. We refactor all deep RDB models to allow the full range of experimental procedures to be applied consistently across all methods. Our findings indicate that the relational transformer (RT) approach delivers the strongest overall performance on both classification and regression tasks compared to the state-of-the-art graph-based modeling and learning of RDBs. Even for single-table learning tasks, deep learning methods designed for RDBs outperform the leading tabular foundation model, TabPFN 2.5 . Extending learning from a single table (hop = 0) to multiple tables (hop = 1, 2) by connecting neighboring tables in relational databases enhances performance, but the additional benefit from higher hops diminishes as computational overhead grows. Deep RDB learning methods have the potential to challenge state-of-the-art tabular foundation models, especially on large-scale enterprise data. The source code for this benchmarking study is publicly available at 1.  \nKeywords relational databases, deep learning, benchmarking, transformer, graph neural networks  \n1 Introduction  \nRelational databases (RDBs) are the backbone of structured data management, where multiple interconnected tables are linked via primary-foreign key relationships. Countless services are performed in e-commerce, healthcare, finance, banking and scientific research by managing data in dozens of tables, millions of rows, and hundreds of columns. [1, 2] . RDBs are designed and optimized to support complex data queries and management, but they do not take into account the requirements of predictive modeling. Many consequential predictive problems, including customer churn forecasting, clinical risk scoring, supply-chain demand estimation, and recommendation, can benefit from learning RDBs [2, 3] . However, the sheer volume and breadth of RDB data spread across dozens of interrelated tables and millions of rows pose a unique challenge for developing big-data-driven predictive models. The fundamental challenge in applying conventional machine learning paradigms to RDBs stems from the structural distinctions between “tabular”and “relational” learning. In a typical tabular structure, each data sample is a self-contained row where samples are assumed to be independent and identically distributed (i.i.d.) . RDBs are different from typical tabular data sets in three different ways. First, an RDB includes multiple related tables linked by constraints of the primary-key and foreign-key (PK–FK), which also contribute the necessary predictive signals to learn an entity type [4] . For example, predicting an entity type, such as customers, would benefit from customer-related information spread across other tables that store  \n1[https://github.com/mdsamad001/Benchmarking-Deep-Relational-Database-Models.git](https://github.com/mdsamad001/Benchmarking-Deep-Relational-Database-Models.git)  \n2 RELATED WORK 2  \n\n| Database | Task | Task Type | Database\u003Cbr>Rows | Task\u003Cbr>Rows | Train\u003Cbr>Rows | Validation\u003Cbr>Rows | Test\u003Cbr>Rows | Sample\u003Cbr>Size |\n| --- | --- | --- | --- | --- | --- | --- | --- | --- |\n| rel-amazon | item-churn | Classification | 15,000,713 | 2,903,795 | 2,5","cbCaicnyo8IhZXn9","https://ap.wps.com/l/cbCaicnyo8IhZXn9","pdf",367842,3,1,10,"English","en",105,"# Introduction\n# Related Work\n# Benchmarking Setup","[{\"question\":\"Why is fair comparison difficult for deep learning methods on relational databases?\",\"answer\":\"Recent deep learning approaches for RDBs have often been evaluated with inconsistent experimental protocols and varying budgets, making comparisons unreliable.\"},{\"question\":\"How does the study ensure consistent evaluation across methods?\",\"answer\":\"All deep RDB models are refactored so the full range of experimental procedures can be applied consistently to every method.\"},{\"question\":\"Which modeling approach performs best in the reported benchmarks?\",\"answer\":\"The relational transformer (RT) provides the strongest overall performance for both classification and regression tasks compared with state-of-the-art graph-based RDB modeling approaches.\"}]",1784182781,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-fair-benchmarking-of-deep-relational-database-learning-models","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,50],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":20},"https://docshare.wps.com/document/research-report/",{"item":51,"name":13,"@type":43,"position":52},"https://docshare.wps.com/document/a-fair-benchmarking-of-deep-relational-database-learning-models/82764/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-22","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is fair comparison difficult for deep learning methods on relational databases?","Question",{"text":75,"@type":76},"Recent deep learning approaches for RDBs have often been evaluated with inconsistent experimental protocols and varying budgets, making comparisons unreliable.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study ensure consistent evaluation across methods?",{"text":80,"@type":76},"All deep RDB models are refactored so the full range of experimental procedures can be applied consistently to every method.",{"name":82,"@type":73,"acceptedAnswer":83},"Which modeling approach performs best in the reported benchmarks?",{"text":84,"@type":76},"The relational transformer (RT) provides the strongest overall performance for both classification and regression tasks compared with state-of-the-art graph-based RDB modeling 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