relational database administration

This work addresses the challenge of verifying relational database management systems’ (RDBMS) compliance with SQL semantics at the standard specification level. We present the first executable Prolog reference implementation grounded in the complete formal SQL semantics defined by ISO/IEC 9075, integrated with differential fuzz testing for semantic-level black-box validation. Unlike prior approaches relying solely on crash detection or meta-transformation, our method enables end-to-end verifiable modeling of SQL standard semantics. Empirical evaluation across MySQL, TiDB, SQLite, and DuckDB uncovered 19 previously unknown vulnerabilities and 11 semantic inconsistencies—each traceable to explicit violations, omissions, or ambiguities in the SQL standard. Our approach significantly enhances the decidability and interpretability of SQL implementation correctness.

Modern database management systems (DBMSs) involve numerous configuration parameters, yet existing auto-tuning approaches either rely on high-overhead data-driven strategies or are constrained by coarse-grained manual heuristics, struggling to balance efficiency and accuracy. This work proposes SysInsight, a novel system that, for the first time, leverages DBMS source code as a knowledge source for tuning. By integrating static code analysis, large language model (LLM)-based semantic reasoning, and association rule mining, SysInsight automatically extracts fine-grained, verifiable quantitative tuning rules and combines them with system diagnostics to enable dynamic online tuning. Experimental results demonstrate that SysInsight achieves an average convergence speed 7.11× faster and a 19.9% higher performance gain compared to state-of-the-art methods.

Contemporary relational database management systems (RDBMSs) suffer from insufficient logical data independence, reducing them to passive storage layers incapable of supporting modern architectural innovation. This paper argues that the Entity-Relationship (ER) model must serve as the native abstraction layer of RDBMSs to overcome this limitation, and it provides the first systematic theoretical justification and empirical validation of the ER model’s necessity and feasibility for ensuring logical independence. Based on this insight, we design and implement ErbiumDB—a prototype system integrating metadata-driven schema management, declarative relational semantic modeling, and runtime relationship evolution. Experimental evaluation demonstrates that ER-based abstraction significantly enhances decoupling between application and storage layers, enabling flexible, semantics-aware data management. ErbiumDB establishes a novel paradigm for intelligent database architectures and delivers a rigorously validated, extensible prototype foundation for future research and development.

Database configuration tuning remains a critical challenge for DBAs, as existing approaches struggle to simultaneously achieve high diagnostic accuracy and actionable remediation recommendations. This paper proposes the first Retrieval-Augmented Generation (RAG) framework specifically designed for DBMS configuration debugging. It integrates heterogeneous knowledge sources—including historical support tickets, official documentation, and real-time telemetry—enabling natural-language-based interactive diagnosis. By combining domain-specific retrieval over heterogeneous documents with fine-tuned large language model (LLM) reasoning, the framework achieves end-to-end problem localization and executable fix generation. Evaluations on a real-world DBMS configuration debugging dataset demonstrate substantial improvements over state-of-the-art baselines: diagnostic accuracy increases by 23.6%, and recommendation adoption rate reaches 89.4%. To our knowledge, this is the first approach to deliver automated configuration debugging that is simultaneously accurate, interpretable, and operationally executable.

Traditional database query optimization relies solely on explicit schema constraints (e.g., primary and foreign keys), overlooking abundant implicit data dependencies—such as ordering or functional dependencies—that remain undetected and unexploited. Method: This paper introduces a workload-driven, lightweight dependency discovery technique that enables millisecond-scale automatic identification of such dependencies. It tightly integrates dependency-aware optimization into both the query optimizer and execution engine, supporting SQL rewriting, dependency propagation, and subquery optimization. Contribution/Results: The approach transcends conventional schema-only optimization by establishing an end-to-end dependency-aware framework. Evaluated across five mainstream DBMSs using standard benchmarks (e.g., TPC-C), it achieves up to 10% higher throughput versus primary/foreign-key–only optimization, 22% over no-dependency optimization, and overall improvements of 5%–33%. Discovery overhead is fully amortized after a single workload execution.

This study addresses the challenge of enforcing data constraints in SQL Server applications by proposing an event-driven approach based on Visual Basic for Applications (VBA). The core innovation lies in the introduction of a novel "constraint-driven design" pseudocode algorithm, which transforms the rigorous enforcement of database constraints into a standardized development workflow. Notably, this algorithm exhibits cross-platform generality, enabling seamless compatibility with NoSQL databases and heterogeneous environments while significantly reducing system adaptation costs. Experimental evaluations demonstrate that the proposed method maintains optimal data quality while validating its versatility and efficiency across diverse platforms. Ultimately, this work provides a cost-effective solution for multi-platform data constraint management, bridging the gap between strict relational integrity requirements and flexible, modern deployment architectures.

This study addresses the challenge of unifying attribute graph models and SQL querying within relational databases. The authors propose reinterpreting SQL foreign key semantics as reference keys, enabling natural modeling of labeled property graphs directly on standard relational table structures. They further extend SQL to support efficient graph data insertion and complex pattern matching. This approach achieves deep integration of relational and graph models within a single system without requiring an additional storage engine. Experimental results demonstrate that the proposed method effectively enables graph structure construction and advanced graph querying capabilities, significantly enhancing relational databases’ support for graph-oriented operations.

This work addresses the limitation of existing Text-to-SQL evaluation benchmarks, which focus narrowly on a single task and overlook critical aspects of the full database lifecycle, including design, operation, and debugging. To bridge this gap, the authors propose DBLifeBench—the first comprehensive evaluation framework encompassing five key phases: design, implementation, execution, debugging, and maintenance. Central to this framework is Progressive-Text2SQL, a novel task grounded in structured reasoning graphs that emulates human iterative problem-solving to narrow the cognitive gap between natural language and complex SQL queries. Experimental results reveal that general-purpose large language models exhibit balanced performance across phases, whereas specialized Text-to-SQL models suffer from catastrophic forgetting outside coding-centric stages. This study establishes a systematic foundation for evaluating full-stack database intelligence.

该研究通过在双范畴数据库模式中引入右伴随以实现通用量化，解决关系除法查询问题，并提供模态算子解释、一阶谓词逻辑及查询优化规则。

This work addresses the challenges posed by the rise of AI-generated queries to the readability and structural explicitness of existing relational query languages. It proposes a unified framework based on Abstract Relational Calculus (ARC) and relational graphs to systematically compare how languages such as SQL, dataframes, and graph query notations express identical query intents. By introducing a formal terminology encompassing information needs, query mappings, and relational schema structures, the study for the first time brings classical database languages and emerging alternatives into a common analytical perspective. The framework is further extended to handle recursive queries, nested relations, and problems beyond PTIME. This contribution establishes a reusable language comparison methodology and a precise design lexicon, offering practical tools for evaluating and designing future relational query languages.