Advanced Database Systems and Queries

Últimos artículos

• Argo-Bench: Evaluating Data Agents on Enterprise-Scale Workflows

  Gabriel Tomitsuka, Arman Raayatsanati, Emma Xing, Duke Gand, Joseph J Ma · 2 de octubre de 2026

  Real-world enterprise data science and analytics workflows require reasoning across dozens of tables, performing statistical analyses, and acting on the results. Established text-to-SQL benchmarks evaluate query generation alone, and audits have found their answer keys frequently wrong. Because real…

• WIP: DBWorkout: A Gamified SQL Practice Platform to Support Formative Learning in Database Courses

  Sehrish Basir Nizamani, Deepika Devaraj, Tien Nguyen, Khyati Goyal, Saad Nizamani, Sally Hamouda, Jaren Goldberg · 2 de octubre de 2026

  This research WIP paper presents DBWorkout, a web-based platform that supports formative SQL learning through sandbox-based execution, automated result-based feedback, and session-based gamification. Learning Structured Query Language (SQL) remains challenging for undergraduate students due to limit…

• TQTS-Bench: A Multi-Syntax Benchmark for Text-to-Query over Time-Series Databases

  Fei Lyu, Zhiyi Peng, Jiaming Liu, Yixuan Yang, Changjian Chen, Zhuo Tang, Jiapeng Zhang, Kenli Li · 30 de septiembre de 2026

  Large language models (LLMs) have significantly advanced natural language querying over relational databases, yet their ability to query time-series databases (TSDBs) remains largely unassessed. Existing benchmarks fail to adequately capture the non-unified query syntaxes, diverse application domain…

• Closing the Cross-Dialect Gap: Query Plans as a Portable Interface in Text-to-SQL

  Corentin Royer (IBM Research, Zurich, Switzerland, ETH Zurich, Zurich, Switzerland), Robin Oester (IBM Research, Zurich, Switzerland), Yotam Perlitz (IBM Research, Zurich, Switzerland), Yannick Metz (ETH Zurich, Zurich, Switzerland), Andrea Giovannini (IBM Research, Zurich, Switzerland), Mennatallah El-Assady (ETH Zurich, Zurich, Switzerland) · 30 de septiembre de 2026

  Text-to-SQL systems are typically trained and evaluated on a single dialect (SQLite), yet production deployments span PostgreSQL, MySQL, ClickHouse, and beyond. We show that this single-dialect assumption leads to a substantial drop in cross-dialect accuracy for every model we tested. The drop persi…

• ModularSQL: A Runtime Guardrail for the Multiplicity Blind Spot in Text-to-SQL

  Tianxin Zhou, Ruixi Lin · 25 de septiembre de 2026

  Text-to-SQL systems are increasingly deployed on production databases, where queries that pass benchmark evaluation can still produce results that distort downstream workflows. Standard set-based execution accuracy (Set-EX) collapses duplicate rows and can therefore miss multiplicity errors, includi…

• KathDB-FAO: Synthesized Query Plans in a Multimodal DBMS

  Guorui Xiao, Douglas Brown, Artur Borycki, Magdalena Balazinska · 25 de septiembre de 2026

  We design, implement, and evaluate KathDB-FAO, a new query evaluation subsystem for our KathDB multimodal DBMS. KathDB-FAO takes as input a query in natural language (NL) and converts it into a query execution plan where each operator is a function whose body is synthesized during query evaluation, …

• Certified Against Which Oracle? Execution Labels Set the Reported Risk of Conformal Abstention for Text-to-SQL

  Jiamiao Liu, Dewen Qiao, Yu Zhang, Xuetao Chen · 23 de septiembre de 2026

  A conformal abstention certificate for text-to-SQL is only as truthful as the correctness labels it is calibrated on. The uncertainty pipelines that read confidence off execution consistency take those labels from the single database a benchmark ships, an oracle known to be lenient. We run a preregi…

• An Iterative LangGraph Agent for Text-to-SQL: Natural Language Access to the Chicago Crime Database

  Vigneshwar Ravi Rao, Rupesh Swarnakar, Fayeq Jeelani Syed{\dag} · 22 de septiembre de 2026

  Non-technical stakeholders frequently cannot write the SQL needed to extract insights from operational databases. We built and evaluated a Text-to-SQL agent that closes this gap end to end: a six-node LangGraph StateGraph checks question relevance, fetches the live schema, generates PostgreSQL, vali…

• Which Part of the Context Layer Does the Work? Separating Semantic Content from Retrieval Scaffolding in Text-to-SQL Agents

  Qing Ye · 22 de septiembre de 2026

  Context layers, curated documentation that an analytics agent fetches at query time, produce large accuracy gains on text-to-SQL benchmarks. A with/without comparison cannot say which part of the layer does the work: the semantic content, the retrieval scaffolding that delivers it, or the pre-comput…

• COAL-SQL: Coverage-Guided Augmentation and Failure-Driven Learning for Text-to-SQL Post-Training

  Qifeng Cai, Xuanguang Pan, Hao Liang, Chang Xu, Wentao Zhang · 21 de septiembre de 2026

  Text-to-SQL translates natural-language questions into executable SQL queries, but open-source large language models still require task-specific post-training for complex, real-world SQL generation. Effective post-training requires both training data that cover the capabilities demanded by the targe…

• The Stochastic Shift: A New Evaluation Paradigm for Text-to-SQL with AI Operators

  Tarfah Alrashed, Fatma Ozcan, Per Jacobsson, Tal Neiman, Xianshun Chen · 21 de septiembre de 2026

  SQL has been augmented with AI operators, enabling modern data analytics platforms to derive insights from both structured and unstructured data. We observe that while current Text-to-SQL systems can successfully generate these AI-augmented queries, reliably evaluating their correctness remains a cr…

• DualSQL: Text-to-SQL with Multi-Agent Reinforcement Learning

  Shijie Chen, Yu Gan, Yeounoh Chung, Jiani Zhang, Quannan Li, Sravan Babu Bodapati, Cody J. Greer, Yu Su, Fatma Ozcan · 17 de septiembre de 2026

  State-of-the-art Text-to-SQL systems are typically multi-agent pipelines centered around two fundamental tasks: schema linking and SQL generation. However, existing work trains separate models for each task, failing to leverage the synergy between these interrelated tasks. In this work, we propose D…

• A Hybrid Dependency-Aware Framework for Task Decomposition and Dynamic Agent Generation in Oracle-to-PostgreSQL Migration

  Oleg Grynets, Oleg Kaskun, Alona Seletska, Daryna Tukalo, Vasyl Lyashkevych · 15 de septiembre de 2026

  Large language model (LLM)-based database migration is often treated as direct code transformation, although enterprise Oracle systems contain heterogeneous SQL and PL/SQL artifacts with different dependencies, execution order, complexity, and validation needs. This paper proposes a hybrid dependenc…

• Beyond Quacking: Deep Integration of Language Models and RAG into DuckDB

  Anas Dorbani, Sunny Yasser, Jimmy Lin, Amine Mhedhbi · 15 de septiembre de 2026

  Knowledge-intensive analytical applications retrieve context from both structured tabular data and unstructured, text-free documents for effective decision-making. Large language models (LLMs) have made it significantly easier to prototype such retrieval and reasoning data pipelines. However, implem…

• What Drives Recovery in Agentic Text-to-Cypher? LAST-CQ: An LLM Agent Self-Refinement Framework

  Ioannis Prokopiou, Athanasios Aidinis, Panagiotis-Christos Kyrmpatsos, Pantelis Vikatos · 14 de septiembre de 2026

  Agentic pipelines for structured-query generation are rapidly expanding, but it is unclear which part of the loop produces the gain. We use LAST-CQ -- a five-agent, training-free, execution-grounded Text-to-Cypher framework -- as an instrumented testbed, running three counterfactuals over 2,471 live…

• Benchmarking Hybrid Deep Research Across Database Querying and Web Search

  Ruofan Wu, Peiran Xu, Xiaolong Li, Fan Shu, Soyoung Yoon, Yite Wang, Xiaodong Yu, Boyi Liu, Feng Yan, Debiao Li, Yuxiong He, Zhewei Yao · 10 de septiembre de 2026

  While autonomous agents have made significant strides in "deep research" by iteratively navigating the open web to synthesize information, real-world problem-solving is rarely confined to a single environment. Complex analytical tasks inherently require agents to weave together evidence from both am…

• SQLMorph: Query Mutation and Fine-Grained Metrics for Text-to-SQL Evaluation

  Mohammadhossein Malekpour, Mohamed Riahi, Maxime Lamothe, Amine Mhedhbi · 9 de septiembre de 2026

  Text-to-SQL systems translate natural language queries into executable SQL, democratizing access to structured data. Despite recent advances driven by large language models (LLMs), evaluation remains a major bottleneck: public benchmarks fail to capture the complexity of enterprise schema, while bui…

• ProcArena: A Multi-Scenario Benchmark for LLMs on Direct and Interactive PL/SQL Development from Natural Language

  Hang Zhang, Chaokun Wang, Yuzhi Pan, Ziyao Zhong, Shuo Cao, Yue Xue, Zeyu Huang, Xingwei Zhou, Fang Niu, Bofan Xie, Guanchen Ge, Leqi Zheng, Ziyang Liu, Xiannian Cao, Pengcheng Ge · 9 de septiembre de 2026

  Large language models (LLMs) have shown strong potential for translating natural-language (NL) requirements into PL/SQL programs, attracting increasing attention from the database community. However, existing NL-to-PL/SQL efforts primarily focus on directly generating PL/SQL from complete NL require…

• SQL-Zero: Self-Evolving Text-to-SQL

  Daniel Machado Pedrozo, Julia Soares Dollis, Bryan Lincoln Marques de Oliveira, Vinicius Alboneti Aguiar, S\'avio Salvarino Teles de Oliveira, Telma Woerle de Lima Soares · 7 de septiembre de 2026

  Training a competitive Text-to-SQL agent usually depends on human-annotated natural-language/SQL pairs, which are expensive, domain-specific, and a bottleneck for scaling to new databases. We show it is possible to train a competitive solver with zero annotated pairs. We introduce SQL-Zero, a propos…

• A Cost-Aware Agentic Architecture for NL-to-SQL over Nested Enterprise Schemas, with a New Benchmark

  Yoga Sri Varshan Varadharajan, Ajay Yadav, Ritesh Goru, Prateek Chaudhury, Constantine Caramanis, Prateek Jain, Divyateja Pasupuleti, Sunil Kumar Pandey · 7 de septiembre de 2026

  Natural-language-to-SQL systems have ad- vanced rapidly on academic benchmarks, yet production enterprise schemas exhibit graph- like, semi-structured, deeply nested structure that current benchmarks do not measure. We make two complementary contributions. First, we introduce the DevRev NL2SQL bench…

• Reflect-SQL: A Self-Reflection Based Framework for Text-to-SQL

  Anupreksha Jain, Manish Shrivastava · 4 de septiembre de 2026

  Democratizing data access through natural language is a crucial goal for modern enterprises, but the practical adoption of Text-to-SQL is critically hindered by real-world complexities: 1. Obscure and large database schemas, 2. Ineffective retrieval of relevant tables and columns due to structured s…

• text2ql: Multi-Target Natural Language Querying via a Language-Agnostic Intermediate Representation

  Ritesh Kumar · 3 de septiembre de 2026

  Natural language interfaces to databases have traditionally suffered from three structural limitations: exclusive targeting of relational SQL, unconditional dependence on large language model (LLM) inference at query time, and absence of any runtime signal when generated queries are semantically inc…

• Git4Data: Database-Native Version Control for AI Agents

  Hongshen Gou, Zuyu Zhang, Yuze Sun, Peng Xu, Feng Tian, Long Wang, Jianguo Wang · 3 de septiembre de 2026

  Large Language Model (LLM) agents increasingly explore many candidate states of relational data in parallel, each of which should remain isolated, reproducible, and auditable, preferably through the same SQL interface used for ordinary data work. Existing tools support this requirement only partiall…

• Zeta-Lite: A Concurrent, Branchable In-Browser SQL Database for Agentic Memory

  Gene Zhang · 3 de septiembre de 2026

  The browser has become a first-class database host: applications increasingly want to store, query, and reason over structured data entirely on the client - for privacy, offline operation, local-first collaboration, and, most recently, as durable memory for in-browser AI agents. One way to get SQL i…

• Replacing Training with Memory: Listwise Selection for Text-to-SQL

  Yeonseok Jeong, Soyoung Yoon, Seongjun Lee, Seung-won Hwang · 2 de septiembre de 2026

  Modern Text-to-SQL systems often follow generate-execute-select pipelines, generating multiple candidate queries then selecting the best one. Listwise selection, by jointly comparing multiple candidates, has been widely adopted, but fine-tuning listwise selectors is costly. We thus propose a fine-tu…