Tootfinder

Confidence Estimation for Text-to-SQL in Large Language Models
Sepideh Entezari Maleki, Mohammadreza Pourreza, Davood Rafiei
https://arxiv.org/abs/2508.14056 https://

Confidence Estimation for Text-to-SQL in Large Language Models
Confidence estimation for text-to-SQL aims to assess the reliability of model-generated SQL queries without having access to gold answers. We study this problem in the context of large language models (LLMs), where access to model weights and gradients is often constrained. We explore both black-box and white-box confidence estimation strategies, evaluating their effectiveness on cross-domain text-to-SQL benchmarks. Our evaluation highlights the superior performance of consistency-based methods…

CRED-SQL: Enhancing Real-world Large Scale Database Text-to-SQL Parsing through Cluster Retrieval and Execution Description
Shaoming Duan, Zirui Wang, Chuanyi Liu, Zhibin Zhu, Yuhao Zhang, Peiyi Han, Liang Yan, Zewu Penge
https://arxiv.org/abs/2508.12769

CRED-SQL: Enhancing Real-world Large Scale Database Text-to-SQL Parsing through Cluster Retrieval and Execution Description
Recent advances in large language models (LLMs) have significantly improved the accuracy of Text-to-SQL systems. However, a critical challenge remains: the semantic mismatch between natural language questions (NLQs) and their corresponding SQL queries. This issue is exacerbated in large-scale databases, where semantically similar attributes hinder schema linking and semantic drift during SQL generation, ultimately reducing model accuracy. To address these challenges, we introduce CRED-SQL, a fr…

@… Wait until you hear how some whackos pronounce SQL.

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[9/10]:
- CRED-SQL: Enhancing Real-world Large Scale Database Text-to-SQL Parsing through Cluster Retrieval...
Shaoming Duan, Zirui Wang, Chuanyi Liu, Zhibin Zhu, Yuhao Zhang, Peiyi Han, Liang Yan, Zewu Penge

Quantum Phase Estimation Beyond the Gaussian Limit
Kimin Park, Tanjung Krisnanda, Yvonne Gao, Radim Filip
https://arxiv.org/abs/2508.13046 https://arxiv.or…

Quantum Phase Estimation Beyond the Gaussian Limit
Quantum metrology aims to enhance measurement precision beyond the standard quantum limit (SQL), the benchmark set by classical resources, enabling advances in sensing, imaging, and fundamental physics. A critical milestone beyond the SQL is surpassing the Gaussian bound -- the fundamental precision limit achievable with any Gaussian state, such as optimally squeezed states. Certain non-Gaussian states, specifically asymmetric superpositions of coherent states (SCS) and superpositions of a vacu…

Software developer Heval Hazal Kurt discusses the pros and cons of relational databases, in comparison with their document-based counterparts, in this July 2025 article. Polyglot systems combining both paradigms are discussed as an ideal solution to differing data access needs within a same project.
"When to Choose NoSQL Over SQL"

PSA for #mechanicalkeyboard enthusiasts who are also using #SQL:
group buy ≠ groupby
(SCNR)

LLM-Driven Data Generation and a Novel Soft Metric for Evaluating Text-to-SQL in Aviation MRO
Patrick Sutanto, Jonathan Kenrick, Max Lorenz, Joan Santoso
https://arxiv.org/abs/2506.13785

LLM-Driven Data Generation and a Novel Soft Metric for Evaluating Text-to-SQL in Aviation MRO
The application of Large Language Models (LLMs) to text-to-SQL tasks promises to democratize data access, particularly in critical industries like aviation Maintenance, Repair, and Operation (MRO). However, progress is hindered by two key challenges: the rigidity of conventional evaluation metrics such as execution accuracy, which offer coarse, binary feedback, and the scarcity of domain-specific evaluation datasets. This paper addresses these gaps. To enable more nuanced assessment, we introdu…

Datrics Text2SQL: A Framework for Natural Language to SQL Query Generation
Tetiana Gladkykh, Kyrylo Kirykov
https://arxiv.org/abs/2506.12234 https://

Datrics Text2SQL: A Framework for Natural Language to SQL Query Generation
Text-to-SQL systems enable users to query databases using natural language, democratizing access to data analytics. However, they face challenges in understanding ambiguous phrasing, domain-specific vocabulary, and complex schema relationships. This paper introduces Datrics Text2SQL, a Retrieval-Augmented Generation (RAG)-based framework designed to generate accurate SQL queries by leveraging structured documentation, example-based learning, and domain-specific rules. The system builds a rich K…

HI-SQL: Optimizing Text-to-SQL Systems through Dynamic Hint Integration
Ganesh Parab, Zishan Ahmad, Dagnachew Birru
https://arxiv.org/abs/2506.18916 https:…

HI-SQL: Optimizing Text-to-SQL Systems through Dynamic Hint Integration
Text-to-SQL generation bridges the gap between natural language and databases, enabling users to query data without requiring SQL expertise. While large language models (LLMs) have significantly advanced the field, challenges remain in handling complex queries that involve multi-table joins, nested conditions, and intricate operations. Existing methods often rely on multi-step pipelines that incur high computational costs, increase latency, and are prone to error propagation. To address these l…

Today’s ride was 8.3 miles… nothing too exciting to report. (In other news I’ve been relearning SQL to pull data from the RunGap SQLite database.)
#biking #bikeTooter #mke

This https://arxiv.org/abs/2505.14690 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csPL_…

SGL: A Structured Graphics Language
This paper introduces SGL, a graphics language that is aesthetically similar to SQL. SGL is based on traditional grammars of graphics, as well as Vega-Lite's composition algebra. SGL demonstrates that the grammatical approach to graphics lends itself naturally to a SQL-like language. As a graphical counterpart to SQL, SGL facilitates the addition of visualization capabilities to SQL query interfaces. This paper presents components of the SGL language alongside examples. Comparisons to SQL and e…

XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQL
Yifu Liu, Yin Zhu, Yingqi Gao, Zhiling Luo, Xiaoxia Li, Xiaorong Shi, Yuntao Hong, Jinyang Gao, Yu Li, Bolin Ding, Jingren Zhou
https://arxiv.org/abs/2507.04701

XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQL
To leverage the advantages of LLM in addressing challenges in the Text-to-SQL task, we present XiYan-SQL, an innovative framework effectively generating and utilizing multiple SQL candidates. It consists of three components: 1) a Schema Filter module filtering and obtaining multiple relevant schemas; 2) a multi-generator ensemble approach generating multiple highquality and diverse SQL queries; 3) a selection model with a candidate reorganization strategy implemented to obtain the optimal SQL q…

Crosslisted article(s) found for cs.DB. https://arxiv.org/list/cs.DB/new
[1/1]:
- Confidence Estimation for Text-to-SQL in Large Language Models
Sepideh Entezari Maleki, Mohammadreza Pourreza, Davood Rafiei

Little #SQLServer Tricks: Fix a Database Stuck in Restoring Mode
https://improveandrepeat.com/2025/07/little-sql-server-tricks-fix-a-datab…

Little SQL Server Tricks: Fix a Database Stuck in Restoring Mode - Improve & Repeat
None

Leveraging large language models for SQL behavior-based database intrusion detection
Meital Shlezinger, Shay Akirav, Lei Zhou, Liang Guo, Avi Kessel, Guoliang Li
https://arxiv.org/abs/2508.05690

Leveraging large language models for SQL behavior-based database intrusion detection
Database systems are extensively used to store critical data across various domains. However, the frequency of abnormal database access behaviors, such as database intrusion by internal and external attacks, continues to rise. Internal masqueraders often have greater organizational knowledge, making it easier to mimic employee behavior effectively. In contrast, external masqueraders may behave differently due to their lack of familiarity with the organization. Current approaches lack the granul…

Don't you love when you're trying to pwn something and stumble across a different bug than the one you were trying to find?
Some years ago I was trying to XSS a webapp and was confronted with a SQL error.
Turns out it *was* XSS-able, but you had to SQL-escape your javascript first.

Note to self: Look up the tech advisor for the movie "Jason Bourne".
#jasonbourne #sql

Invocable APIs derived from NL2SQL datasets for LLM Tool-Calling Evaluation
Benjamin Elder, Anupama Murthi, Jungkoo Kang, Ankita Rajaram Naik, Kiran Kate, Kinjal Basu, Danish Contractor
https://arxiv.org/abs/2506.11266

Invocable APIs derived from NL2SQL datasets for LLM Tool-Calling Evaluation
Large language models (LLMs) are routinely deployed as agentic systems, with access to tools that interact with live environments to accomplish tasks. In enterprise deployments these systems need to interact with API collections that can be extremely large and complex, often backed by databases. In order to create datasets with such characteristics, we explore how existing NL2SQL (Natural Language to SQL query) datasets can be used to automatically create NL2API datasets. Specifically, this wor…

🔧 Integrate tools to your agent in less than 10 lines of code - reuse between multiple agents or frameworks
💬 Query databases in plain English directly from your #IDE - no SQL writing needed

SQL-Exchange: Transforming SQL Queries Across Domains
Mohammadreza Daviran, Brian Lin, Davood Rafiei
https://arxiv.org/abs/2508.07087 https://arxiv.org/pdf…

SQL-Exchange: Transforming SQL Queries Across Domains
We introduce SQL-Exchange, a framework for mapping SQL queries across different database schemas by preserving the source query structure while adapting domain-specific elements to align with the target schema. We investigate the conditions under which such mappings are feasible and beneficial, and examine their impact on enhancing the in-context learning performance of text-to-SQL systems as a downstream task. Our comprehensive evaluation across multiple model families and benchmark datasets--…

Day 16
Just published a deep dive into building a secure login page with Next.js, NestJS, JWT, and PostgreSQL.
- Email verification
- Role-based access control
- Subscription enforcement
- Token decoding in frontend
- SQL-level inserts for system roles
Includes full code snippets and explanation of the entire flow.
Perfect if you're working on full-stack apps with JavaScript, TypeScript, and SQL.

Focus and Context and LLMs | Taras' Blog on AI, Perf, Hacks
#AI

Im using case_when() quite a lot, case_match() is new to me: #rstats

A general vectorised switch() — case_match
This function allows you to vectorise multiple switch() statements. Each case is evaluated sequentially and the first match for each element determines the corresponding value in the output vector. If no cases match, the .default is used. case_match() is an R equivalent of the SQL "simple" CASE WHEN statement. Connection to case_when() While case_when() uses logical expressions on the left-hand side of the formula, case_match() uses values to match against .x with. The following two statement…

New chart library for SQL notebook https://www.belle-nuit.com/sql-notebook/index.html?url=../site/files/chart-library.json

Spark SQL pipe (|>) for Spark 4.0.0?!
https://issues.apache.org/jira/browse/SPARK-49555
https://

SLM-SQL: An Exploration of Small Language Models for Text-to-SQL
Lei Sheng, Shuai-Shuai Xu
https://arxiv.org/abs/2507.22478 https://arxiv.org/pdf/2507.2247…

SLM-SQL: An Exploration of Small Language Models for Text-to-SQL
Large language models (LLMs) have demonstrated strong performance in translating natural language questions into SQL queries (Text-to-SQL). In contrast, small language models (SLMs) ranging from 0.5B to 1.5B parameters currently underperform on Text-to-SQL tasks due to their limited logical reasoning capabilities. However, SLMs offer inherent advantages in inference speed and suitability for edge deployment. To explore their potential in Text-to-SQL applications, we leverage recent advancements…

@… This is very cool!
I did something similar for PostgreSQL last month.
https://codeberg.org/ooble/tremuloides

SQLord: A Robust Enterprise Text-to-SQL Solution via Reverse Data Generation and Workflow Decomposition
Song Cheng, Qiannan Cheng, Linbo Jin, Lei Yi, Guannan Zhang
https://arxiv.org/abs/2507.10629

SQLord: A Robust Enterprise Text-to-SQL Solution via Reverse Data Generation and Workflow Decomposition
Transforming natural language into SQL queries (NL2SQL) is crucial for data-driven business applications. Existing frameworks, trained on open-source datasets, struggle with complex business logic and lack domain-specific data for fine-tuning. Additionally, evaluation methods often require annotated data and executable database environments, which are scarce in real-world scenarios. To address these challenges, we propose SQLord, an enterprise-level NL2SQL framework. First, SQLord introduces a …

Improving Table Retrieval with Question Generation from Partial Tables
Hsing-Ping Liang, Che-Wei Chang, Yao-Chung Fan
https://arxiv.org/abs/2508.06168 https://

Improving Table Retrieval with Question Generation from Partial Tables
Recent advances in open-domain question answering over tables have widely adopted large language models (LLMs) under the Retriever-Reader architecture. Prior works have effectively leveraged LLMs to tackle the complex reasoning demands of the Reader component, such as text-to-text, text-to-SQL, and multi hop reasoning. In contrast, the Retriever component has primarily focused on optimizing the query representation-training retrievers to retrieve relevant tables based on questions, or to select…

It's the year 2025 and Microsoft have released version 21 (Twenty One!) of Sql Server Management Studio and apparently they've lost the secret to the technology that makes it so that when you double click a file to open it, it doesn't start a whole new instance but instead opens the file in the running instance
I mean you read the stories about how NASA forgot how to be able to read the tapes full of Mars data or whatever but you don't expect it to happen to something y…

This https://arxiv.org/abs/2412.05561 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

Can the Rookies Cut the Tough Cookie? Exploring the Use of LLMs for SQL Equivalence Checking
Equivalence checking of SQL queries is an intractable problem often encountered in settings ranging from grading SQL submissions to debugging query optimizers. Despite recent work toward developing practical solutions, only simple queries written using a small subset of SQL are supported, leaving the equivalence checking of sophisticated SQL queries at the mercy of intensive, potentially error-prone, manual analysis. In this paper, we explore how LLMs can be used to reason with SQL queries to a…

Replaced article(s) found for cs.PL. https://arxiv.org/list/cs.PL/new
[1/1]:
- MCTS-SQL: Light-Weight LLMs can Master the Text-to-SQL through Monte Carlo Tree Search
Shuozhi Yuan, Limin Chen, Miaomiao Yuan, Jin Zhao

My experience is limited to ASP.NET, SQL, and some Python at a Fortune 500 company, so take this with a grain of salt. When he talks about agents, it sounds like automation to me. I’ve been writing jobs (or agents) for decades to run automated tasks on a schedule. If you want to add another point of failure into your job, knock yourself out. Also, I’m wary of encouraging neophytes to outsource work they don’t know how to do. That’s a recipe for disaster.

SQLBarber: A System Leveraging Large Language Models to Generate Customized and Realistic SQL Workloads
Jiale Lao, Immanuel Trummer
https://arxiv.org/abs/2507.06192

SQLBarber: A System Leveraging Large Language Models to Generate Customized and Realistic SQL Workloads
Database research and development often require a large number of SQL queries for benchmarking purposes. However, acquiring real-world SQL queries is challenging due to privacy concerns, and existing SQL generation methods are limited in customization and in satisfying realistic constraints. To address this issue, we present SQLBarber, a system based on Large Language Models (LLMs) to generate customized and realistic SQL workloads. SQLBarber (i) eliminates the need for users to manually craft …

https://tighten.com/insights/sql-generated-columns-and-views-how-to-use-them-in-your-laravel-app/

SQL Notebook: Random cluster in PostScript
https://www.belle-nuit.com/sql-notebook/index.html?url=../site/files/random-cluster.json

Lightweight Transformers for Zero-Shot and Fine-Tuned Text-to-SQL Generation Using Spider
Chirag Seth, Utkarsh Singh
https://arxiv.org/abs/2508.04623 https://

Lightweight Transformers for Zero-Shot and Fine-Tuned Text-to-SQL Generation Using Spider
Text-to-SQL translation enables non-expert users to query relational databases using natural language, with applications in education and business intelligence. This study evaluates three lightweight transformer models - T5-Small, BART-Small, and GPT-2 - on the Spider dataset, focusing on low-resource settings. We developed a reusable, model-agnostic pipeline that tailors schema formatting to each model's architecture, training them across 1000 to 5000 iterations and evaluating on 1000 test sam…

Qymera: Simulating Quantum Circuits using RDBMS
Tim Littau, Rihan Hai
https://arxiv.org/abs/2506.08759 https://arxiv.org/pdf/2506.087…

Qymera: Simulating Quantum Circuits using RDBMS
Quantum circuit simulation is crucial for quantum computing such as validating quantum algorithms. We present Qymera, a system that repurposes relational database management systems (RDBMSs) for simulation by translating circuits into SQL queries, allowing quantum operations to run natively within an RDBMS. Qymera supports a wide range of quantum circuits, offering a graphical circuit builder and code-based interfaces to input circuits. With a benchmarking framework, Qymera facilitates comparis…

Interactive Text-to-SQL via Expected Information Gain for Disambiguation
Luyu Qiu, Jianing Li, Chi Su, Lei Chen
https://arxiv.org/abs/2507.06467 https://…

Interactive Text-to-SQL via Expected Information Gain for Disambiguation
Relational databases are foundational to numerous domains, including business intelligence, scientific research, and enterprise systems. However, accessing and analyzing structured data often requires proficiency in SQL, which is a skill that many end users lack. With the development of Natural Language Processing (NLP) technology, the Text-to-SQL systems attempt to bridge this gap by translating natural language questions into executable SQL queries via an automated algorithm. Yet, when operat…

GPU Acceleration of SQL Analytics on Compressed Data
Zezhou Huang, Krystian Sakowski, Hans Lehnert, Wei Cui, Carlo Curino, Matteo Interlandi, Marius Dumitru, Rathijit Sen
https://arxiv.org/abs/2506.10092

GPU Acceleration of SQL Analytics on Compressed Data
GPUs are uniquely suited to accelerate (SQL) analytics workloads thanks to their massive compute parallelism and High Bandwidth Memory (HBM) -- when datasets fit in the GPU HBM, performance is unparalleled. Unfortunately, GPU HBMs remain typically small when compared with lower-bandwidth CPU main memory. Besides brute-force scaling across many GPUs, current solutions to accelerate queries on large datasets include leveraging data partitioning and loading smaller data batches in GPU HBM, and hyb…

Confidence Scoring for LLM-Generated SQL in Supply Chain Data Extraction
Jiekai Ma, Yikai Zhao
https://arxiv.org/abs/2506.17203 https://

Confidence Scoring for LLM-Generated SQL in Supply Chain Data Extraction
Large Language Models (LLMs) have recently enabled natural language interfaces that translate user queries into executable SQL, offering a powerful solution for non-technical stakeholders to access structured data. However, one of the limitation that LLMs do not natively express uncertainty makes it difficult to assess the reliability of their generated queries. This paper presents a case study that evaluates multiple approaches to estimate confidence scores for LLM-generated SQL in supply chai…

E3-Rewrite: Learning to Rewrite SQL for Executability, Equivalence,and Efficiency
Dongjie Xu, Yue Cui, Weijie Shi, Qingzhi Ma, Hanghui Guo, Jiaming Li, Yao Zhao, Ruiyuan Zhang, Shimin Di, Jia Zhu, Kai Zheng, Jiajie Xu
https://arxiv.org/abs/2508.09023

E3-Rewrite: Learning to Rewrite SQL for Executability, Equivalence,and Efficiency
SQL query rewriting aims to reformulate a query into a more efficient form while preserving equivalence. Most existing methods rely on predefined rewrite rules. However, such rule-based approaches face fundamental limitations: (1) fixed rule sets generalize poorly to novel query patterns and struggle with complex queries; (2) a wide range of effective rewriting strategies cannot be fully captured by declarative rules. To overcome these issues, we propose using large language models (LLMs) to ge…

This https://arxiv.org/abs/2506.03308 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Hermes: High-Performance Homomorphically Encrypted Vector Databases
Fully Homomorphic Encryption (FHE) has long promised the ability to compute over encrypted data without revealing sensitive contents -- a foundational goal for secure cloud analytics. Yet despite decades of cryptographic advances, practical integration of FHE into real-world relational databases remains elusive. This paper presents \textbf{Hermes}, the first system to enable FHE-native vector query processing inside a standard SQL engine. By leveraging the multi-slot capabilities of modern sche…

Text-to-SQL Task-oriented Dialogue Ontology Construction
Renato Vukovic, Carel van Niekerk, Michael Heck, Benjamin Ruppik, Hsien-Chin Lin, Shutong Feng, Nurul Lubis, Milica Gasic
https://arxiv.org/abs/2507.23358

Text-to-SQL Task-oriented Dialogue Ontology Construction
Large language models (LLMs) are widely used as general-purpose knowledge sources, but they rely on parametric knowledge, limiting explainability and trustworthiness. In task-oriented dialogue (TOD) systems, this separation is explicit, using an external database structured by an explicit ontology to ensure explainability and controllability. However, building such ontologies requires manual labels or supervised training. We introduce TeQoDO: a Text-to-SQL task-oriented Dialogue Ontology constr…

SWE-SQL: Illuminating LLM Pathways to Solve User SQL Issues in Real-World Applications
Jinyang Li, Xiaolong Li, Ge Qu, Per Jacobsson, Bowen Qin, Binyuan Hui, Shuzheng Si, Nan Huo, Xiaohan Xu, Yue Zhang, Ziwei Tang, Yuanshuai Li, Florensia Widjaja, Xintong Zhu, Feige Zhou, Yongfeng Huang, Yannis Papakonstantinou, Fatma Ozcan, Chenhao Ma, Reynold Cheng

SWE-SQL: Illuminating LLM Pathways to Solve User SQL Issues in Real-World Applications
Resolution of complex SQL issues persists as a significant bottleneck in real-world database applications. Current Large Language Models (LLMs), while adept at text-to-SQL translation, have not been rigorously evaluated on the more challenging task of debugging SQL issues. To address this gap, we introduce BIRD-CRITIC, a new SQL issue debugging benchmark comprising 530 PostgreSQL tasks (BIRD-CRITIC-PG) and 570 multi-dialect tasks (BIRD-CRITIC-Multi), distilled from authentic user issues and rep…

Replaced article(s) found for cs.DB. https://arxiv.org/list/cs.DB/new
[1/1]:
- EllieSQL: Cost-Efficient Text-to-SQL with Complexity-Aware Routing
Yizhang Zhu, Runzhi Jiang, Boyan Li, Nan Tang, Yuyu Luo

SQL Notebook pattern fill
https://www.belle-nuit.com/sql-notebook/index.html?url=../site/files/pattern-fill.json

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[3/3]:
- Structure Guided Large Language Model for SQL Generation
Qinggang Zhang, Hao Chen, Junnan Dong, Shengyuan Chen, Feiran Huang, Xiao Huang

A Learned Cost Model-based Cross-engine Optimizer for SQL Workloads
Andr\'as Strausz, Niels Pardon, Ioana Giurgiu
https://arxiv.org/abs/2506.02802 http…

A Learned Cost Model-based Cross-engine Optimizer for SQL Workloads
Lakehouse systems enable the same data to be queried with multiple execution engines. However, selecting the engine best suited to run a SQL query still requires a priori knowledge of the query computational requirements and an engine capability, a complex and manual task that only becomes more difficult with the emergence of new engines and workloads. In this paper, we address this limitation by proposing a cross-engine optimizer that can automate engine selection for diverse SQL queries throu…

eSapiens: A Real-World NLP Framework for Multimodal Document Understanding and Enterprise Knowledge Processing
Isaac Shi, Zeyuan Li, Wenli Wang, Lewei He, Yang Yang, Tianyu Shi
https://arxiv.org/abs/2506.16768

eSapiens: A Real-World NLP Framework for Multimodal Document Understanding and Enterprise Knowledge Processing
We introduce eSapiens, a unified question-answering system designed for enterprise settings, which bridges structured databases and unstructured textual corpora via a dual-module architecture. The system combines a Text-to-SQL planner with a hybrid Retrieval-Augmented Generation (RAG) pipeline, enabling natural language access to both relational data and free-form documents. To enhance answer faithfulness, the RAG module integrates dense and sparse retrieval, commercial reranking, and a citatio…

Random Optical 6
https://www.belle-nuit.com/sql-notebook/index.html?url=../site/files/random-optical-6.json

Raqlet: Cross-Paradigm Compilation for Recursive Queries
Amir Shaikhha, Youning Xia, Meisam Tarabkhah, Jazal Saleem, Anna Herlihy
https://arxiv.org/abs/2508.03978 https://

Raqlet: Cross-Paradigm Compilation for Recursive Queries
We introduce Raqlet, a source-to-source compilation framework that addresses the fragmentation of recursive querying engines spanning relational (recursive SQL), graph (Cypher, GQL), and deductive (Datalog) systems. Recent standards such as SQL:2023's SQL/PGQ and the GQL standard provide a common foundation for querying graph data within relational and graph databases; however, real-world support remains inconsistent across systems. Raqlet bridges this gap by translating recursive queries acros…

This https://arxiv.org/abs/2505.19988 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

Automatic Metadata Extraction for Text-to-SQL
Large Language Models (LLMs) have recently become sophisticated enough to automate many tasks ranging from pattern finding to writing assistance to code generation. In this paper, we examine text-to-SQL generation. We have observed from decades of experience that the most difficult part of query development lies in understanding the database contents. These experiences inform the direction of our research. Text-to-SQL benchmarks such as SPIDER and Bird contain extensive metadata that is gener…

A Functional Data Model and Query Language is All You Need
Jens Dittrich
https://arxiv.org/abs/2507.20671 https://arxiv.org/pdf/2507.20671

A Functional Data Model and Query Language is All You Need
We propose the vision of a functional data model (FDM) and an associated functional query language (FQL). Our proposal has far-reaching consequences: we show a path to come up with a modern QL that solves (almost if not) all problems of SQL (NULL-values, impedance mismatch, SQL injection, missing querying capabilities for updates, etc.). FDM and FQL are much more expressive than the relational model and SQL. In addition, in contrast to SQL, FQL integrates smoothly into existing programming lang…

#SQL Generated Columns & Views in #Laravel #php #mysql

Reasoning-Table: Exploring Reinforcement Learning for Table Reasoning
Fangyu Lei, Jinxiang Meng, Yiming Huang, Tinghong Chen, Yun Zhang, Shizhu He, Jun Zhao, Kang Liu
https://arxiv.org/abs/2506.01710

Reasoning-Table: Exploring Reinforcement Learning for Table Reasoning
Table reasoning, encompassing tasks such as table question answering, fact verification, and text-to-SQL, requires precise understanding of structured tabular data, coupled with numerical computation and code manipulation for effective inference. Supervised fine-tuning (SFT) approaches have achieved notable success but often struggle with generalization and robustness due to biases inherent in imitative learning. We introduce Reasoning-Table, the first application of reinforcement learning (RL)…

Plots in SQL Notebook
https://www.belle-nuit.com/sql-notebook/index.html?url=../site/files/plots.json

TailorSQL: An NL2SQL System Tailored to Your Query Workload
Kapil Vaidya, Jialin Ding, Sebastian Kosak, David Kernert, Chuan Lei, Xiao Qin, Abhinav Tripathy, Ramesh Balan, Balakrishnan Narayanaswamy, Tim Kraska
https://arxiv.org/abs/2505.23039

TailorSQL: An NL2SQL System Tailored to Your Query Workload
NL2SQL (natural language to SQL) translates natural language questions into SQL queries, thereby making structured data accessible to non-technical users, serving as the foundation for intelligent data applications. State-of-the-art NL2SQL techniques typically perform translation by retrieving database-specific information, such as the database schema, and invoking a pre-trained large language model (LLM) using the question and retrieved information to generate the SQL query. However, existin…

SQL notebook can now also do mathematical notation
https://www.belle-nuit.com/sql-notebook/index.html?url=../site/files/mathematical-notation.json

Query, Don't Train: Privacy-Preserving Tabular Prediction from EHR Data via SQL Queries
Josefa Lia Stoisser, Marc Boubnovski Martell, Kaspar M\"artens, Lawrence Phillips, Stephen Michael Town, Rory Donovan-Maiye, Julien Fauqueur
https://arxiv.org/abs/2505.21801

Query, Don't Train: Privacy-Preserving Tabular Prediction from EHR Data via SQL Queries
Electronic health records (EHRs) contain richly structured, longitudinal data essential for predictive modeling, yet stringent privacy regulations (e.g., HIPAA, GDPR) often restrict access to individual-level records. We introduce Query, Don't Train (QDT): a structured-data foundation-model interface enabling tabular inference via LLM-generated SQL over EHRs. Instead of training on or accessing individual-level examples, QDT uses a large language model (LLM) as a schema-aware query planner to g…

Beyond Natural Language Plans: Structure-Aware Planning for Query-Focused Table Summarization
Weijia Zhang, Songgaojun Deng, Evangelos Kanoulas
https://arxiv.org/abs/2507.22829 …

Beyond Natural Language Plans: Structure-Aware Planning for Query-Focused Table Summarization
Query-focused table summarization requires complex reasoning, often approached through step-by-step natural language (NL) plans. However, NL plans are inherently ambiguous and lack structure, limiting their conversion into executable programs like SQL and hindering scalability, especially for multi-table tasks. To address this, we propose a paradigm shift to structured representations. We introduce a new structured plan, TaSoF, inspired by formalism in traditional multi-agent systems, and a fra…

How to use Linux on iPad to convert PNG to GIF
This is a proof of concept. When you create animations in PostScript on SQL Notebook, you can download the PNG images as ZIP file.
Now, If I installed Linux and ffmpeg on iPad, would that work? As a matter of fact, I will show you that it is possible, but it is very slow.
https://www.belle-nuit.com/sql-notebook/index.html?url=../site/files/how-to-use-linux-on-ipad-to-convert-png-to-gif.json

Replaced article(s) found for cs.DB. https://arxiv.org/list/cs.DB/new
[1/1]:
- MCTS-SQL: Light-Weight LLMs can Master the Text-to-SQL through Monte Carlo Tree Search
Shuozhi Yuan, Limin Chen, Miaomiao Yuan, Jin Zhao

While I was browsing for LaTeX samples of charts, I found this periodic table of elements and was quite surprised looking at the complexity of the source code. I thought PostScript could do more readable code.
https://www.belle-nuit.com/sql-notebook/index.html?url=../site/files/periodic-table-of-elements.json

LINEAGEX: A Column Lineage Extraction System for SQL
Shi Heng Zhang, Zhengjie Miao, Jiannan Wang
https://arxiv.org/abs/2505.23133 https://

LINEAGEX: A Column Lineage Extraction System for SQL
As enterprise data grows in size and complexity, column-level data lineage, which records the creation, transformation, and reference of each column in the warehouse, has been the key to effective data governance that assists tasks like data quality monitoring, storage refactoring, and workflow migration. Unfortunately, existing systems introduce overheads by integration with query execution or fail to achieve satisfying accuracy for column lineage. In this paper, we demonstrate LINEAGEX, a lig…

SQL notebook: rounded polygons
https://www.belle-nuit.com/sql-notebook/index.html?url=../site/files/round-polygons.json

QUITE: A Query Rewrite System Beyond Rules with LLM Agents
Yuyang Song, Hanxu Yan, Jiale Lao, Yibo Wang, Yufei Li, Yuanchun Zhou, Jianguo Wang, Mingjie Tang
https://arxiv.org/abs/2506.07675

QUITE: A Query Rewrite System Beyond Rules with LLM Agents
Query rewrite transforms SQL queries into semantically equivalent forms that run more efficiently. Existing approaches mainly rely on predefined rewrite rules, but they handle a limited subset of queries and can cause performance regressions. This limitation stems from three challenges of rule-based query rewrite: (1) it is hard to discover and verify new rules, (2) fixed rewrite rules do not generalize to new query patterns, and (3) some rewrite techniques cannot be expressed as fixed rules. M…

Replaced article(s) found for cs.DB. https://arxiv.org/list/cs.DB/new
[1/1]:
- Structure Guided Large Language Model for SQL Generation
Qinggang Zhang, Hao Chen, Junnan Dong, Shengyuan Chen, Feiran Huang, Xiao Huang

SQL notebook 3D rendering
https://www.belle-nuit.com/sql-notebook/index.html?url=../site/files/3d-rendering.json

This https://arxiv.org/abs/2505.21801 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

Query, Don't Train: Privacy-Preserving Tabular Prediction from EHR Data via SQL Queries
Electronic health records (EHRs) contain richly structured, longitudinal data essential for predictive modeling, yet stringent privacy regulations (e.g., HIPAA, GDPR) often restrict access to individual-level records. We introduce Query, Don't Train (QDT): a structured-data foundation-model interface enabling tabular inference via LLM-generated SQL over EHRs. Instead of training on or accessing individual-level examples, QDT uses a large language model (LLM) as a schema-aware query planner to g…

Training-Free Query Optimization via LLM-Based Plan Similarity
Nikita Vasilenko, Alexander Demin, Vladimir Boorlakov
https://arxiv.org/abs/2506.05853 https…

Training-Free Query Optimization via LLM-Based Plan Similarity
Large language model (LLM) embeddings offer a promising new avenue for database query optimization. In this paper, we explore how pre-trained execution plan embeddings can guide SQL query execution without the need for additional model training. We introduce LLM-PM (LLM-based Plan Mapping), a framework that embeds the default execution plan of a query, finds its k nearest neighbors among previously executed plans, and recommends database hintsets based on neighborhood voting. A lightweight cons…

LLM4Hint: Leveraging Large Language Models for Hint Recommendation in Offline Query Optimization
Suchen Liu, Jun Gao, Yinjun Han, Yang Lin
https://arxiv.org/abs/2507.03384

LLM4Hint: Leveraging Large Language Models for Hint Recommendation in Offline Query Optimization
Query optimization is essential for efficient SQL query execution in DBMS, and remains attractive over time due to the growth of data volumes and advances in hardware. Existing traditional optimizers struggle with the cumbersome hand-tuning required for complex workloads, and the learning-based methods face limitations in ensuring generalization. With the great success of Large Language Model (LLM) across diverse downstream tasks, this paper explores how LLMs can be incorporated to enhance the …

Rethinking Analytical Processing in the GPU Era
Bobbi Yogatama, Yifei Yang, Kevin Kristensen, Devesh Sarda, Abigale Kim, Adrian Cockcroft, Yu Teng, Joshua Patterson, Gregory Kimball, Wes McKinney, Weiwei Gong, Xiangyao Yu
https://arxiv.org/abs/2508.04701

Rethinking Analytical Processing in the GPU Era
The era of GPU-powered data analytics has arrived. In this paper, we argue that recent advances in hardware (e.g., larger GPU memory, faster interconnect and IO, and declining cost) and software (e.g., composable data systems and mature libraries) have removed the key barriers that have limited the wider adoption of GPU data analytics. We present Sirius, a prototype open-source GPU-native SQL engine that offers drop-in acceleration for diverse data systems. Sirius treats GPU as the primary engi…

SSCard: Substring Cardinality Estimation using Suffix Tree-Guided Learned FM-Index
Yirui Zhan, Wen Nie, Jun Gao
https://arxiv.org/abs/2505.24312 https://…

SSCard: Substring Cardinality Estimation using Suffix Tree-Guided Learned FM-Index
Accurate cardinality estimation of substring queries, which are commonly expressed using the SQL LIKE predicate, is crucial for query optimization in database systems. While both rule-based methods and machine learning-based methods have been developed to optimize various aspects of cardinality estimation, their absence of error bounds may result in substantial estimation errors, leading to suboptimal execution plans. In this paper, we propose SSCard, a novel SubString Cardinality estimator tha…

GaussMaster: An LLM-based Database Copilot System
Wei Zhou, Ji Sun, Xuanhe Zhou, Guoliang Li, Luyang Liu, Hao Wu, Tianyuan Wang
https://arxiv.org/abs/2506.23322

GaussMaster: An LLM-based Database Copilot System
In the financial industry, data is the lifeblood of operations, and DBAs shoulder significant responsibilities for SQL tuning, database deployment, diagnosis, and service repair. In recent years, both database vendors and customers have increasingly turned to autonomous database platforms in an effort to alleviate the heavy workload of DBAs. However, existing autonomous database platforms are limited in their capabilities, primarily addressing single-point issues such as NL2SQL, anomaly detecti…

An advanced AI driven database system
M. Tedeschi, S. Rizwan, C. Shringi, V. Devram Chandgir, S. Belich
https://arxiv.org/abs/2507.17778 https://arxiv.org/…

An advanced AI driven database system
Contemporary database systems, while effective, suffer severe issues related to complexity and usability, especially among individuals who lack technical expertise but are unfamiliar with query languages like Structured Query Language (SQL). This paper presents a new database system supported by Artificial Intelligence (AI), which is intended to improve the management of data using natural language processing (NLP) - based intuitive interfaces, and automatic creation of structured queries and s…

PBench: Workload Synthesizer with Real Statistics for Cloud Analytics Benchmarking
Yan Zhou, Chunwei Liu, Bhuvan Urgaonkar, Zhengle Wang, Magnus Mueller, Chao Zhang, Songyue Zhang, Pascal Pfeil, Dominik Horn, Zhengchun Liu, Davide Pagano, Tim Kraska, Samuel Madden, Ju Fan
https://arxiv.org/abs/2506.16379

PBench: Workload Synthesizer with Real Statistics for Cloud Analytics Benchmarking
Cloud service providers commonly use standard benchmarks like TPC-H and TPC-DS to evaluate and optimize cloud data analytics systems. However, these benchmarks rely on fixed query patterns and fail to capture the real execution statistics of production cloud workloads. Although some cloud database vendors have recently released real workload traces, these traces alone do not qualify as benchmarks, as they typically lack essential components like the original SQL queries and their underlying dat…