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Eng."],"published-print":{"date-parts":[[2025,6,19]]},"abstract":"<jats:p>\n                    Optimizing object-relational database mapping (ORM) design is crucial for performance and scalability in modern software systems. However, widely used ORM tools offer limited support for exploring performance tradeoffs, often enforcing a single design and overlooking alternatives, which can lead to suboptimal outcomes. While systematic tradeoff analysis can reveal Pareto-optimal designs, its high computational cost and poor scalability hinder practical adoption. This paper presents\n                    <jats:sc>DesignTradeoffSculptor,<\/jats:sc>\n                    an extensible tool suite for efficient, scalable tradeoff analysis in ORM database design. Leveraging advanced Transformer-based deep learning models\u2014trained and fine-tuned on formally analyzed database designs\u2014and framing design exploration as a Natural Language Processing task,\n                    <jats:sc>DesignTradeoffSculptor<\/jats:sc>\n                    efficiently identifies and removes suboptimal designs, sharply reducing the number of candidates requiring costly tradeoff analysis. Experiments show that\n                    <jats:sc>DesignTradeoffSculptor<\/jats:sc>\n                    uncovers optimal designs missed by leading ORM tools and improves analysis efficiency by over 98.21%, reducing tradeoff analysis time from 15 days to just 18 minutes, demonstrating the transformative potential of integrating formal methods with deep learning.\n                  <\/jats:p>","DOI":"10.1145\/3729344","type":"journal-article","created":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T11:15:34Z","timestamp":1750331734000},"page":"1639-1662","source":"Crossref","is-referenced-by-count":5,"title":["Unlocking Optimal ORM Database Designs: Accelerated Tradeoff Analysis with Transformers"],"prefix":"10.1145","volume":"2","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3417-4352","authenticated-orcid":false,"given":"Md Rashedul","family":"Hasan","sequence":"first","affiliation":[{"name":"University of Nebraska-Lincoln, School of Computing, Lincoln, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9818-9600","authenticated-orcid":false,"given":"Mohammad Rashedul","family":"Hasan","sequence":"additional","affiliation":[{"name":"University of Nebraska-Lincoln, Department of Electrical and Computer Engineering, Lincoln, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6686-466X","authenticated-orcid":false,"given":"Hamid","family":"Bagheri","sequence":"additional","affiliation":[{"name":"University of Nebraska-Lincoln, School of Computing, Lincoln, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,6,19]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","unstructured":"2025. 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