Tootfinder

Little #SQLServer Tricks: Escape _ in LIKE Queries
https://improveandrepeat.com/2025/09/little-sql-server-tricks-escape-underline-in-like-querie…

Little SQL Server Tricks: Escape _ in LIKE Queries - Improve & Repeat
None

PARROT: A Benchmark for Evaluating LLMs in Cross-System SQL Translation
Wei Zhou, Guoliang Li, Haoyu Wang, Yuxing Han, Xufei Wu, Fan Wu, Xuanhe Zhou
https://arxiv.org/abs/2509.23338

PARROT: A Benchmark for Evaluating LLMs in Cross-System SQL Translation
Large language models (LLMS) have shown increasing effectiveness in Text-to-SQL tasks. However, another closely related problem, Cross-System SQL Translation (a.k.a., SQL-to-SQL), which adapts a query written for one database system (e.g., MySQL) into its equivalent one for another system (e.g., ClickHouse), is of great practical importance but remains underexplored. Existing SQL benchmarks are not well-suited for SQL-to-SQL evaluation, which (1) focus on a limited set of database systems (ofte…

MaskSQL: Safeguarding Privacy for LLM-Based Text-to-SQL via Abstraction
Sepideh Abedini (University of Waterloo, Vector Institute), Shubhankar Mohapatra (University of Waterloo), D. B. Emerson (Vector Institute), Masoumeh Shafieinejad (Vector Institute), Jesse C. Cresswell (Layer 6 AI), Xi He (University of Waterloo, Vector Institute)
https://

MaskSQL: Safeguarding Privacy for LLM-Based Text-to-SQL via Abstraction
Large language models (LLMs) have shown promising performance on tasks that require reasoning, such as text-to-SQL, code generation, and debugging. However, regulatory frameworks with strict privacy requirements constrain their integration into sensitive systems. State-of-the-art LLMs are also proprietary, costly, and resource-intensive, making local deployment impractical. Consequently, utilizing such LLMs often requires sharing data with third-party providers, raising privacy concerns and ris…

SQL Notebook new integrated functionalities: serialize, deserialize, regression, movingaverage.
L’API JS de AlaSQL makes it very easy to extend SQL: not only functions and aggregators, but also INTO targets and FROM sources.
https://www.belle-nuit.com/sql-notebook/index.html?url=../site/files/sql-extensions.json&autorun=1

#SQL Anti-Patterns You Should Avoid
https://datamethods.substack.com/p/sql-anti-patterns-you-should-avoid

Quantum-Limited Optical Vector Analysis
Karthik Dasigi, Pavel A. Dmitriev, Kah Jen Wo, Fumiya Hanamura, Lingda Kong, Steven Touzard
https://arxiv.org/abs/2509.25950 https://

Quantum-Limited Optical Vector Analysis
Optical Vector Analysers (OVA) are critical for emerging technologies such as integrated photonics and optical positioning. Achieving a sensitivity near the Standard Quantum Limit (SQL) while acquiring a wide spectrum allows an accurate measurement of targets that are fragile, non-linear, or that scatter most of the probe light away. Existing OVAs operate with a sensitivity orders of magnitude below the SQL. In this paper, we use a free-running interferometer with a frequency range of 20 THz as…

Leveraging LLMs for Automated Translation of Legacy Code: A Case Study on PL/SQL to Java Transformation
Lola Solovyeva, Eduardo Carneiro Oliveira, Shiyu Fan, Alper Tuncay, Shamil Gareev, Andrea Capiluppi
https://arxiv.org/abs/2508.19663

Leveraging LLMs for Automated Translation of Legacy Code: A Case Study on PL/SQL to Java Transformation
The VT legacy system, comprising approximately 2.5 million lines of PL/SQL code, lacks consistent documentation and automated tests, posing significant challenges for refactoring and modernisation. This study investigates the feasibility of leveraging large language models (LLMs) to assist in translating PL/SQL code into Java for the modernised "VTF3" system. By leveraging a dataset comprising 10 PL/SQL-to-Java code pairs and 15 Java classes, which collectively established a domain model for th…

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…

SteinerSQL: Graph-Guided Mathematical Reasoning for Text-to-SQL Generation
Xutao Mao, Tao Liu, Hongying Zan
https://arxiv.org/abs/2509.19623 https://arxiv.…

SteinerSQL: Graph-Guided Mathematical Reasoning for Text-to-SQL Generation
Large Language Models (LLMs) struggle with complex Text-to-SQL queries that demand both sophisticated mathematical reasoning and intricate schema navigation. Existing methods often tackle these challenges in isolation, creating a fractured reasoning process that compromises logical and structural correctness. To resolve this, we introduce SteinerSQL, a framework that unifies these dual challenges into a single, graph-centric optimization problem. SteinerSQL operates in three stages: mathematica…

from my link log —
A novel technique for SQL injection with PHP PDO’s prepared statements.
https://slcyber.io/assetnote-security-research-center/a-novel-technique-for-sql-injection-in-pdos-prepared-statem…

VizGen: Data Exploration and Visualization from Natural Language via a Multi-Agent AI Architecture
Sandaru Fernando, Imasha Jayarathne, Sithumini Abeysekara, Shanuja Sithamparanthan, Thushari Silva, Deshan Jayawardana
https://arxiv.org/abs/2509.22218

VizGen: Data Exploration and Visualization from Natural Language via a Multi-Agent AI Architecture
Data visualization is essential for interpreting complex datasets, yet traditional tools often require technical expertise, limiting accessibility. VizGen is an AI-assisted graph generation system that empowers users to create meaningful visualizations using natural language. Leveraging advanced NLP and LLMs like Claude 3.7 Sonnet and Gemini 2.0 Flash, it translates user queries into SQL and recommends suitable graph types. Built on a multi-agent architecture, VizGen handles SQL generation, gra…

Text-to-SQL Oriented to the Process Mining Domain: A PT-EN Dataset for Query Translation
Bruno Yui Yamate, Thais Rodrigues Neubauer, Marcelo Fantinato, Sarajane Marques Peres
https://arxiv.org/abs/2509.09684

Text-to-SQL Oriented to the Process Mining Domain: A PT-EN Dataset for Query Translation
This paper introduces text-2-SQL-4-PM, a bilingual (Portuguese-English) benchmark dataset designed for the text-to-SQL task in the process mining domain. Text-to-SQL conversion facilitates natural language querying of databases, increasing accessibility for users without SQL expertise and productivity for those that are experts. The text-2-SQL-4-PM dataset is customized to address the unique challenges of process mining, including specialized vocabularies and single-table relational structures …

Just about, erm, Go time at Gophercon 2025 Day 1! yesterday was Day 0 of course as this isn't a SQL conference. #GophersUnite

This is just a lovely api, turning swift strongly typed APIs into safe SQL queries:
https://swiftpackageindex.com/pointfreeco/swift-structured-queries/main/documentation/structuredqueriescore/

The purpose of an ORM is to use an SQL database without benefiting from SQL database.
#ORM #SQL #Programming

SteinerSQL: Graph-Guided Mathematical Reasoning for Text-to-SQL Generation
Xutao Mao, Tao Liu, Hongying Zan
https://arxiv.org/abs/2509.19623 https://arxiv.…

SteinerSQL: Graph-Guided Mathematical Reasoning for Text-to-SQL Generation
Large Language Models (LLMs) struggle with complex Text-to-SQL queries that demand both sophisticated mathematical reasoning and intricate schema navigation. Existing methods often tackle these challenges in isolation, creating a fractured reasoning process that compromises logical and structural correctness. To resolve this, we introduce SteinerSQL, a framework that unifies these dual challenges into a single, graph-centric optimization problem. SteinerSQL operates in three stages: mathematica…

Bootstrapping Learned Cost Models with Synthetic SQL Queries
Michael Nidd, Christoph Miksovic, Thomas Gschwind, Francesco Fusco, Andrea Giovannini, Ioana Giurgiu
https://arxiv.org/abs/2508.19807

Bootstrapping Learned Cost Models with Synthetic SQL Queries
Having access to realistic workloads for a given database instance is extremely important to enable stress and vulnerability testing, as well as to optimize for cost and performance. Recent advances in learned cost models have shown that when enough diverse SQL queries are available, one can effectively and efficiently predict the cost of running a given query against a specific database engine. In this paper, we describe our experience in exploiting modern synthetic data generation techniques,…

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…

🚀 90-second installation process
Single artisan command board generation
Automatic database collation handling
📊 MySQL/MariaDB with utf8mb4_bin collation
PostgreSQL with C collation
SQLite with binary collation
SQL Server with Latin1_General_BIN2
🌐 https://github.com/Relaticle/flowforge

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…

AmbiSQL: Interactive Ambiguity Detection and Resolution for Text-to-SQL
Zhongjun Ding, Yin Lin, Tianjing Zeng
https://arxiv.org/abs/2508.15276 https://arxi…

AmbiSQL: Interactive Ambiguity Detection and Resolution for Text-to-SQL
Text-to-SQL systems translate natural language questions into SQL queries, providing substantial value for non-expert users. While large language models (LLMs) show promising results for this task, they remain error-prone. Query ambiguity has been recognized as a major obstacle for LLM-based Text-to-SQL systems, leading to misinterpretation of user intent and inaccurate SQL generation. We demonstrate AmbiSQL, an interactive system that automatically detects query ambiguities and guides users th…

DeKeyNLU: Enhancing Natural Language to SQL Generation through Task Decomposition and Keyword Extraction
Jian Chen, Zhenyan Chen, Xuming Hu, Peilin Zhou, Yining Hua, Han Fang, Cissy Hing Yee Choy, Xinmei Ke, Jingfeng Luo, Zixuan Yuan
https://arxiv.org/abs/2509.14507

DeKeyNLU: Enhancing Natural Language to SQL Generation through Task Decomposition and Keyword Extraction
Natural Language to SQL (NL2SQL) provides a new model-centric paradigm that simplifies database access for non-technical users by converting natural language queries into SQL commands. Recent advancements, particularly those integrating Retrieval-Augmented Generation (RAG) and Chain-of-Thought (CoT) reasoning, have made significant strides in enhancing NL2SQL performance. However, challenges such as inaccurate task decomposition and keyword extraction by LLMs remain major bottlenecks, often lea…

Multi-Stage CD-Kennedy Receiver for QPSK Modulated CV-QKD in Turbulent Channels
Renzhi Yuan, Zhixing Wang, Shouye Miao, Mufei Zhao, Haifeng Yao, Bin Cao, Mugen Peng
https://arxiv.org/abs/2509.20030

Multi-Stage CD-Kennedy Receiver for QPSK Modulated CV-QKD in Turbulent Channels
Continuous variable-quantum key distribution (CV-QKD) protocols attract increasing attentions in recent years because they enjoy high secret key rate (SKR) and good compatibility with existing optical communication infrastructure. Classical coherent receivers are widely employed in coherent states based CV-QKD protocols, whose detection performance is bounded by the standard quantum limit (SQL). Recently, quantum receivers based on displacement operators are experimentally demonstrated with det…

📊 Supports #MySQL #PostgreSQL #SQLite and SQL Server with automatic collations
🛠️ Works with existing Laravel models and database
No complex setup required
⚡ Handles 100 cards…

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

Changing the Paradigm from Dynamic Queries to LLM-generated SQL Queries with Human Intervention
Ambre Assor, Hyeon Jeon, Sungbok Shin, Jean-Daniel Fekete
https://arxiv.org/abs/2509.09461

Changing the Paradigm from Dynamic Queries to LLM-generated SQL Queries with Human Intervention
We propose leveraging Large Language Models (LLMs) as an interaction layer for medical visualization systems. In domains like healthcare, where users must navigate high-dimensional, coded, and heterogeneous datasets, LLM-generated queries enable expert medical users to express complex analytical intents in natural language. These intents are then translated into editable and executable queries, replacing the dynamic query interfaces used by traditional visualization systems built around sliders…

SPFT-SQL: Enhancing Large Language Model for Text-to-SQL Parsing by Self-Play Fine-Tuning
Yuhao Zhang, Shaoming Duan, Jinhang Su, Chuanyi Liu, Peiyi Han
https://arxiv.org/abs/2509.03937

SPFT-SQL: Enhancing Large Language Model for Text-to-SQL Parsing by Self-Play Fine-Tuning
Despite the significant advancements of self-play fine-tuning (SPIN), which can transform a weak large language model (LLM) into a strong one through competitive interactions between models of varying capabilities, it still faces challenges in the Text-to-SQL task. SPIN does not generate new information, and the large number of correct SQL queries produced by the opponent model during self-play reduces the main model's ability to generate accurate SQL queries. To address this challenge, we prop…

TPSQLi: Test Prioritization for SQL Injection Vulnerability Detection in Web Applications
Guan-Yan Yang, Farn Wang, You-Zong Gu, Ya-Wen Teng, Kuo-Hui Yeh, Ping-Hsueh Ho, Wei-Ling Wen
https://arxiv.org/abs/2509.10920

TPSQLi: Test Prioritization for SQL Injection Vulnerability Detection in Web Applications
The rapid proliferation of network applications has led to a significant increase in network attacks. According to the OWASP Top 10 Projects report released in 2021, injection attacks rank among the top three vulnerabilities in software projects. This growing threat landscape has increased the complexity and workload of software testing, necessitating advanced tools to support agile development cycles. This paper introduces a novel test prioritization method for SQL injection vulnerabilities to…

Text to Query Plans for Question Answering on Large Tables
Yipeng Zhang, Chen Wang, Yuzhe Zhang, Jacky Jiang
https://arxiv.org/abs/2508.18758 https://arxiv…

Text to Query Plans for Question Answering on Large Tables
Efficient querying and analysis of large tabular datasets remain significant challenges, especially for users without expertise in programming languages like SQL. Text-to-SQL approaches have shown promising performance on benchmark data; however, they inherit SQL's drawbacks, including inefficiency with large datasets and limited support for complex data analyses beyond basic querying. We propose a novel framework that transforms natural language queries into query plans. Our solution is implem…

Mein Hochrechnungstool auf SQL-Notebook für die heutigen Abstimmungsresultate
https://www.belle-nuit.com/sql-notebook/index.html?url=../site/files/hochrechnungstool.json

from my link log —
Runtime-extensible PEG parsers for SQL.
https://duckdb.org/2024/11/22/runtime-extensible-parsers.html
saved 2024-11-22 …

Runtime-Extensible SQL Parsers Using PEG
Despite their central role in processing queries, parsers have not received any noticeable attention in the data systems space. State-of-the art systems are content with ancient old parser generators. These generators create monolithic, inflexible and unforgiving parsers that hinder innovation in query languages and frustrate users. Instead, parsers should be rewritten using modern abstractions like Parser Expression Grammars (PEG), which allow dynamic changes to the accepted query syntax and b…

Today I took a very long & complex SQL query (generated by some long & complex code) and packed it into a CTE, so we can (in one special case) do some calculation using a window function to weed out duplicates. I'm actually amazed that it worked in the end...

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

Crosslisted article(s) found for cs.DB. https://arxiv.org/list/cs.DB/new
[1/1]:
- A State-of-the-Art SQL Reasoning Model using RLVR
Alnur Ali, et al.
https://

Learning from Few Samples: A Novel Approach for High-Quality Malcode Generation
Haijian Ma, Daizong Liu, Xiaowen Cai, Pan Zhou, Yulai Xie
https://arxiv.org/abs/2508.18148 https:…

Learning from Few Samples: A Novel Approach for High-Quality Malcode Generation
Intrusion Detection Systems (IDS) play a crucial role in network security defense. However, a significant challenge for IDS in training detection models is the shortage of adequately labeled malicious samples. To address these issues, this paper introduces a novel semi-supervised framework \textbf{GANGRL-LLM}, which integrates Generative Adversarial Networks (GANs) with Large Language Models (LLMs) to enhance malicious code generation and SQL Injection (SQLi) detection capabilities in few-sampl…

By accident I just did:
DELETE FROM tablename WHERE "foo";
on a oroduction database. when I saw:
Query OK, 387 rows affected, 2 warnings (0.01 sec)
my heart skipped. Missing conditional. Should have said "WHERE x="foo". Affected all rows.
Hooray for backups.
(Yes, I shouldn't be typing SQL at a production DB.)

@… please, is this the SQL-related issue?
<https://github.com/freebsd/pkg/issues/2522>
― pkg: sqlite error while executing BEGIN IMMEDIATE TRANSACT…

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…

It is good IT Security practice to separate data from code. For example, an SQL Injection attack is to get the target to treat data (entered into a form) as code, that is break that barrier.
Now take an "AI agent" doing a task for you like: "Download that podcast that Frank emailed me about". It will read untrusted data (e-mails in my inbox), access sensitive data (e-mails from my friends) get more stuff from the web (the podcast episode), etc.
And all that in an environment where there 1/2

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

HES-SQL: Hybrid Reasoning for Efficient Text-to-SQL with Structural Skeleton Guidance
Suming Qiu, Jing Li, Zhicheng Zhou, Junjie Huang, Linyuan Qiu, Zhijie Sun
https://arxiv.org/abs/2510.08896

HES-SQL: Hybrid Reasoning for Efficient Text-to-SQL with Structural Skeleton Guidance
We present HES-SQL, a novel hybrid training framework that advances Text-to-SQL generation through the integration of thinking-mode-fused supervised fine-tuning (SFT) with Group Relative Policy Optimization (GRPO). Our approach introduces three key innovations: (1) a skeleton-completeness scoring mechanism that enhances preference alignment between generated queries and optimal SQL structures; (2) a query-latency-aware reward system that incentivizes the generation of computationally efficient …

Just because you can doesn't mean you should:
https://cedardb.com/blog/doomql/

Building a DOOM-like multiplayer shooter in pure SQL
CedarDB is a database system that delivers unmatched performance for transactions and analytics, from small writes to handling billions of rows. Built on cutting-edge research to power today’s tools and tomorrow’s challenges.

Practical robust Bayesian spin-squeezing-enhanced quantum sensing under noises
Jinye Wei, Jungeng Zhou, Yi Shen, Jiahao Huang, Chaohong Lee
https://arxiv.org/abs/2509.08316 http…

Practical robust Bayesian spin-squeezing-enhanced quantum sensing under noises
Spin-squeezed states constitute a valuable entanglement resource capable of surpassing the standard quantum limit (SQL). However, spin-squeezed states only enable sub-SQL uncertainty within a narrow parametric window near some specific points. Identifying optimal measurement protocols for spin-squeezed states remains an outstanding challenge. Here we present an adaptive Bayesian quantum estimation protocol that achieves optimal measurement precision with spin-squeezed states under noises. Our p…

Do LLMs Align with My Task? Evaluating Text-to-SQL via Dataset Alignment
Davood Rafiei, Morgan Lindsay Heisler, Weiwei Zhang, Mohammadreza Pourreza, Yong Zhang
https://arxiv.org/abs/2510.04919

Do LLMs Align with My Task? Evaluating Text-to-SQL via Dataset Alignment
Supervised Fine-Tuning (SFT) is an effective method for adapting Large Language Models (LLMs) on downstream tasks. However, variability in training data can hinder a model's ability to generalize across domains. This paper studies the problem of dataset alignment for Natural Language to SQL (NL2SQL or text to SQL), examining how well SFT training data matches the structural characteristics of target queries and how this alignment impacts model performance. We hypothesize that alignment can be a…

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

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--…

Installed SSMS 22 preview.
Did they finally fire the person who was keeping the results pane white in dark mode? Sorry SQL Shades, looks like someone finally listened.

SQL Notebook 2.2 avec des tables en Wikitext et un meilleur rendu des polices dans les navigateurs autres que Safari https://www.belle-nuit.com/sql-notebook/index.html

Question for the ether: I have an *old* WordPress site backed up. Not sure on exact version, but from 2009. I have all of the files, but no database (or at least no .SQL export file; I may have the literal files of the db, not sure).
I want to run the site locally on Windows, so that I can see what it looked like. Don't really need content, just mainly the layout and page structure, CSS, basically the theme is what I want to recover. Is this possible? #wordpress #indieweb

Play by the Type Rules: Inferring Constraints for LLM Functions in Declarative Programs
Parker Glenn, Alfy Samuel, Daben Liu
https://arxiv.org/abs/2509.20208 https://

Play by the Type Rules: Inferring Constraints for LLM Functions in Declarative Programs
Integrating LLM powered operators in declarative query languages allows for the combination of cheap and interpretable functions with powerful, generalizable language model reasoning. However, in order to benefit from the optimized execution of a database query language like SQL, generated outputs must align with the rules enforced by both type checkers and database contents. Current approaches address this challenge with orchestrations consisting of many LLM-based post-processing calls to ensu…

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…

Optimization techniques for SQL ML queries: A performance analysis of real-time feature computation in OpenMLDB
Mashkhal A. Sidiq, Aras A. Salih, Samrand M. Hassan
https://arxiv.org/abs/2509.15529

Optimization techniques for SQL+ML queries: A performance analysis of real-time feature computation in OpenMLDB
In this study, we optimize SQL+ML queries on top of OpenMLDB, an open-source database that seamlessly integrates offline and online feature computations. The work used feature-rich synthetic dataset experiments in Docker, which acted like production environments that processed 100 to 500 records per batch and 6 to 12 requests per batch in parallel. Efforts have been concentrated in the areas of better query plans, cached execution plans, parallel processing, and resource management. The experim…

Quantum-enhanced parameter estimation in continuously monitored boundary time crystals
Eoin O'Connor, Victor Montenegro, Francesco Albarelli, Matteo G. A. Paris, Abolfazl Bayat, Marco G. Genoni
https://arxiv.org/abs/2508.15448

Quantum-enhanced parameter estimation in continuously monitored boundary time crystals
We investigate quantum-enhanced parameter estimation in boundary time crystals (BTCs) via continuous monitoring. By analytically deriving the global quantum Fisher information rate, we show that in the time-crystal phase the ultimate precision exhibits a cubic scaling with the system size, $f_{\mathrm{global}}\sim N^3$, surpassing both the sensitivity at the critical point and the standard quantum limit (SQL). We then numerically demonstrate that this bound can be attained already at finite $N$…

Agentic LLMs for Question Answering over Tabular Data
Rishit Tyagi, Mohit Gupta, Rahul Bouri
https://arxiv.org/abs/2509.09234 https://arxiv.org/pdf/2509.09…

Agentic LLMs for Question Answering over Tabular Data
Question Answering over Tabular Data (Table QA) presents unique challenges due to the diverse structure, size, and data types of real-world tables. The SemEval 2025 Task 8 (DataBench) introduced a benchmark composed of large-scale, domain-diverse datasets to evaluate the ability of models to accurately answer structured queries. We propose a Natural Language to SQL (NL-to-SQL) approach leveraging large language models (LLMs) such as GPT-4o, GPT-4o-mini, and DeepSeek v2:16b to generate SQL queri…

SQL-of-Thought: Multi-agentic Text-to-SQL with Guided Error Correction
Saumya Chaturvedi, Aman Chadha, Laurent Bindschaedler
https://arxiv.org/abs/2509.00581 https://

SQL-of-Thought: Multi-agentic Text-to-SQL with Guided Error Correction
Converting natural language queries into SQL queries is a crucial challenge in both industry and academia, aiming to increase access to databases and large-scale applications. This work examines how in-context learning and chain-of-thought can be utilized to develop a robust solution for text-to-SQL systems. We propose SQL-of-Thought: a multi-agent framework that decomposes the Text2SQL task into schema linking, subproblem identification, query plan generation, SQL generation, and a guided corr…

Today we had the first day of the Koha DACH Hackfest in Göttingen. After a tour through the data center we started various discussions on Koha. The first feature that was requested was something I actually implemented some time ago (passing SQL to the ElasticSearch indexer), but failed to add to a weird wrapper script I did not know existed. Which I primptly fixed:

STARQA: A Question Answering Dataset for Complex Analytical Reasoning over Structured Databases
Mounica Maddela, Lingjue Xie, Daniel Preotiuc-Pietro, Mausam
https://arxiv.org/abs/2509.19508

STARQA: A Question Answering Dataset for Complex Analytical Reasoning over Structured Databases
Semantic parsing methods for converting text to SQL queries enable question answering over structured data and can greatly benefit analysts who routinely perform complex analytics on vast data stored in specialized relational databases. Although several benchmarks measure the abilities of text to SQL, the complexity of their questions is inherently limited by the level of expressiveness in query languages and none focus explicitly on questions involving complex analytical reasoning which requir…

Crosslisted article(s) found for cs.PF. https://arxiv.org/list/cs.PF/new
[1/1]:
- Speeding up SQL subqueries via decoupling of non-correlated predicate (extended version)
Radivonchik, Kuzin, Chizhov, Shcheka, Firsov, Smirnov, Chernishev

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…

RubikSQL: Lifelong Learning Agentic Knowledge Base as an Industrial NL2SQL System
Zui Chen, Han Li, Xinhao Zhang, Xiaoyu Chen, Chunyin Dong, Yifeng Wang, Xin Cai, Su Zhang, Ziqi Li, Chi Ding, Jinxu Li, Shuai Wang, Dousheng Zhao, Sanhai Gao, Guangyi Liu
https://arxiv.org/abs/2508.17590

RubikSQL: Lifelong Learning Agentic Knowledge Base as an Industrial NL2SQL System
We present RubikSQL, a novel NL2SQL system designed to address key challenges in real-world enterprise-level NL2SQL, such as implicit intents and domain-specific terminology. RubikSQL frames NL2SQL as a lifelong learning task, demanding both Knowledge Base (KB) maintenance and SQL generation. RubikSQL systematically builds and refines its KB through techniques including database profiling, structured information extraction, agentic rule mining, and Chain-of-Thought (CoT)-enhanced SQL profiling.…

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…

Exploring Database Normalization Effects on SQL Generation
Ryosuke Kohita
https://arxiv.org/abs/2510.01989 https://arxiv.org/pdf/2510.01989

Exploring Database Normalization Effects on SQL Generation
Schema design, particularly normalization, is a critical yet often overlooked factor in natural language to SQL (NL2SQL) systems. Most prior research evaluates models on fixed schemas, overlooking the influence of design on performance. We present the first systematic study of schema normalization's impact, evaluating eight leading large language models on synthetic and real-world datasets with varied normalization levels. We construct controlled synthetic datasets with formal normalization (1N…

SQLGovernor: An LLM-powered SQL Toolkit for Real World Application
Jie Jiang, Siqi Shen, Haining Xie, Yang Li, Yu Shen, Danqing Huang, Bo Qian, Yinjun Wu, Wentao Zhang, Bin Cui, Peng Chen
https://arxiv.org/abs/2509.08575

SQLGovernor: An LLM-powered SQL Toolkit for Real World Application
SQL queries in real world analytical environments, whether written by humans or generated automatically often suffer from syntax errors, inefficiency, or semantic misalignment, especially in complex OLAP scenarios. To address these challenges, we propose SQLGovernor, an LLM powered SQL toolkit that unifies multiple functionalities, including syntax correction, query rewriting, query modification, and consistency verification within a structured framework enhanced by knowledge management. SQLGov…

QCardEst/QCardCorr: Quantum Cardinality Estimation and Correction
Tobias Winker, Jinghua Groppe, Sven Groppe
https://arxiv.org/abs/2509.08817 https://arxiv…

QCardEst/QCardCorr: Quantum Cardinality Estimation and Correction
Cardinality estimation is an important part of query optimization in DBMS. We develop a Quantum Cardinality Estimation (QCardEst) approach using Quantum Machine Learning with a Hybrid Quantum-Classical Network. We define a compact encoding for turning SQL queries into a quantum state, which requires only qubits equal to the number of tables in the query. This allows the processing of a complete query with a single variational quantum circuit (VQC) on current hardware. In addition, we compare mu…

Design a dashboard with SQL Notebook
https://www.belle-nuit.com/sql-notebook/index.html?url=../site/files/design-a-dashboard.json

Evaluating NL2SQL via SQL2NL
Mohammadtaher Safarzadeh, Afshin Oroojlooyjadid, Dan Roth
https://arxiv.org/abs/2509.04657 https://arxiv.org/pdf/2509.04657

Evaluating NL2SQL via SQL2NL
Robust evaluation in the presence of linguistic variation is key to understanding the generalization capabilities of Natural Language to SQL (NL2SQL) models, yet existing benchmarks rarely address this factor in a systematic or controlled manner. We propose a novel schema-aligned paraphrasing framework that leverages SQL-to-NL (SQL2NL) to automatically generate semantically equivalent, lexically diverse queries while maintaining alignment with the original schema and intent. This enables the fi…

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…

Crosslisted article(s) found for cs.DB. https://arxiv.org/list/cs.DB/new
[1/1]:
- Chain-of-Query: Unleashing the Power of LLMs in SQL-Aided Table Understanding via Multi-Agent Col...
Songyuan Sui, Hongyi Liu, Serena Liu, Li Li, Soo-Hyun Choi, Rui Chen, Xia Hu

Thinkquel: A Model Dedicated to Text-to-dbt Using Synthetic Data and a Span-Aware Objective
Anni Li, Aria Attar, Paul Dong
https://arxiv.org/abs/2510.00186 https://

Thinkquel: A Model Dedicated to Text-to-dbt Using Synthetic Data and a Span-Aware Objective
Transforming natural-language requests into reliable, production-ready data transformations remains challenging: correctness depends on precise schema linking and warehouse-specific SQL dialects, while the strongest supervision available during training--execution success and result matching--are provided only at the sequence level. At the same time, assembling large, execution-validated corpora is costly, and token-level objectives misalign with these global signals, yielding unstable optimiza…

Speeding up SQL subqueries via decoupling of non-correlated predicate (extended version)
Dmitrii Radivonchik, Yakov Kuzin, Anton Chizhov, Dmitriy Shcheka, Mikhail Firsov, Kirill Smirnov, George Chernishev
https://arxiv.org/abs/2510.05907

Speeding up SQL subqueries via decoupling of non-correlated predicate (extended version)
In this paper, we discuss a novel technique for processing correlated subqueries in SQL. The core idea is to isolate the non-correlated part of the predicate and use it to reduce the number of evaluations of the correlated part. We begin by providing an overview of several classes of queries that may benefit from this technique. For each class, we propose a potential rewrite and discuss the conditions under which it is advantageous. Next, we address the evaluation aspects of the proposed rewrit…

Einführung in die Datenanalyse mit SQL
Dieser Kurs richtet sich an Personen, die beruflich oder aus persönlichem Interesse Daten analysieren möchten und mit den bisher benutzten Instrumenten wie Excel an Grenzen stossen.
Als Vorbedingung braucht es keine Programmierkenntnisse. Es ist sinnvoll, wenn Sie schon mit Excel-Dateien zu tun gehabt haben und eine Vorstellung davon haben, was Formeln sind.
https://www.belle-nuit.com/einfuehrung-in-die-datenanalyse-mit-sql

AQORA: A Learned Adaptive Query Optimizer for Spark SQL
Jiahao He, Yutao Cui, Cuiping Li, Jikang Jiang, Yuheng Hou, Hong Chen
https://arxiv.org/abs/2510.10580 https://

AQORA: A Learned Adaptive Query Optimizer for Spark SQL
Recent studies have identified two main approaches to improve query optimization: learned query optimization (LQO), which generates or selects better query plans before execution based on models trained in advance, and adaptive query processing (AQP), which adapts the query plan during execution based on statistical feedback collected at runtime. Although both approaches have shown promise, they also face critical limitations. LQO must commit to a fixed plan without access to actual cardinaliti…

OASIS: Object-based Analytics Storage for Intelligent SQL Query Offloading in Scientific Tabular Workloads
Soon Hwang, Junhyeok Park, Junghyun Ryu, Seonghoon Ahn, Jeoungahn Park, Jeongjin Lee, Soonyeal Yang, Jungki Noh, Woosuk Chung, Hoshik Kim, Youngjae Kim
https://arxiv.org/abs/2509.01966

OASIS: Object-based Analytics Storage for Intelligent SQL Query Offloading in Scientific Tabular Workloads
Computation-Enabled Object Storage (COS) systems, such as MinIO and Ceph, have recently emerged as promising storage solutions for post hoc, SQL-based analysis on large-scale datasets in High-Performance Computing (HPC) environments. By supporting object-granular layouts, COS facilitates column-oriented access and supports in-storage execution of data reduction operators, such as filters, close to where the data resides. Despite growing interest and adoption, existing COS systems exhibit severa…

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…

Fisher test on SQL notebook
https://www.belle-nuit.com/sql-notebook/index.html?url=../site/files/fisher-test.json

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

SABER: A SQL-Compatible Semantic Document Processing System Based on Extended Relational Algebra
Changjae Lee, Zhuoyue Zhao, Jinjun Xiong
https://arxiv.org/abs/2509.00277 https:…

SABER: A SQL-Compatible Semantic Document Processing System Based on Extended Relational Algebra
The emergence of large-language models (LLMs) has enabled a new class of semantic data processing systems (SDPSs) to support declarative queries against unstructured documents. Existing SDPSs are, however, lacking a unified algebraic foundation, making their queries difficult to compose, reason, and optimize. We propose a new semantic algebra, SABER (Semantic Algebra Based on Extended Relational algebra), opening the possibility of semantic operations' logical plan construction, optimization, a…

Important update to SQL Notebook 2.0. Image data, Form data, Help menu, Present menu (fullscreen) and automatic handling of drop table on create table and create table on select into
https://www.belle-nuit.com/sql-notebook/index.html

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

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

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

Replaced article(s) found for cs.DB. https://arxiv.org/list/cs.DB/new
[1/1]:
- Proving correctness for SQL implementations of OCL constraints
Hoang Nguyen, Manuel Clavel

On the Expressiveness of Languages for Querying Property Graphs in Relational Databases
Hadar Rotschield, Liat Peterfreund
https://arxiv.org/abs/2510.07062 https://

On the Expressiveness of Languages for Querying Property Graphs in Relational Databases
SQL/PGQ is the emerging ISO standard for querying property graphs defined as views over relational data. We formalize its expressive power across three fragments: the read-only core, the read-write extension, and an extended variant with richer view definitions. Our results show that graph creation plays a central role in determining the expressiveness. The read-only fragment is strictly weaker than the read-write fragment, and the latter is still below the complexity class NL. Extending view d…

Relational Algebras for Subset Selection and Optimisation
David Robert Pratten, Luke Mathieson, Fahimeh Ramezani
https://arxiv.org/abs/2509.06439 https://a…

Relational Algebras for Subset Selection and Optimisation
The database community lacks a unified relational query language for subset selection and optimisation queries, limiting both user expression and query optimiser reasoning about such problems. Decades of research (latterly under the rubric of prescriptive analytics) have produced powerful evaluation algorithms with incompatible, ad-hoc SQL extensions that specify and filter through distinct mechanisms. We present the first unified algebraic foundation for these queries, introducing relational e…

CARPO: Leveraging Listwise Learning-to-Rank for Context-Aware Query Plan Optimization
Wenrui Zhou, Qiyu Liu, Jingshu Peng, Aoqian Zhang, Lei Chen
https://arxiv.org/abs/2509.03102

CARPO: Leveraging Listwise Learning-to-Rank for Context-Aware Query Plan Optimization
Efficient data processing is increasingly vital, with query optimizers playing a fundamental role in translating SQL queries into optimal execution plans. Traditional cost-based optimizers, however, often generate suboptimal plans due to flawed heuristics and inaccurate cost models, leading to the emergence of Learned Query Optimizers (LQOs). To address challenges in existing LQOs, such as the inconsistency and suboptimality inherent in pairwise ranking methods, we introduce CARPO, a generic fr…

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…