relational databases

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 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.

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 query plan representations are highly fragmented, impeding test method reuse and cross-system analysis. Method: This paper proposes the first database-agnostic unified query plan representation framework, systematically identifying the “operator–attribute–format” trinity as the common structural foundation across execution plans. It abstracts internal plans from nine mainstream databases via cross-database reverse parsing and intermediate representation modeling, yielding an extensible, formally verifiable unified model. Contribution/Results: The framework enables seamless reuse of existing testing methodologies across all nine databases, uncovering 17 previously undetected, database-specific defects. It facilitates rapid adaptation of multi-database visualization tools and supports standardized comparative analysis—including semantic alignment and performance profiling—of query plans across heterogeneous systems.

This paper addresses the challenge of data integration across heterogeneous databases. It proposes an algebraic approach grounded in category theory and functional programming. The method models database schemas and instances as many-sorted equational theories and their initial algebras, respectively, and employs adjoint functors to enable rigorous cross-schema data migration. Innovatively, it unifies category theory, many-sorted equational logic, and functional programming paradigms; introduces a pushout-based schema mapping construction; and defines an algebraic query language—with for/where/return syntax—endowed with formal semantics. The authors implement AQL, an open-source tool supporting formally specified schema mappings, automated data migration, and verifiable query compilation. This framework constitutes the first theoretically rigorous integration of these three foundational paradigms, simultaneously ensuring mathematical precision and enhancing the automation and reliability of data integration.

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.

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。

本文提出了一种基于标记有向树的新型数据库模型，通过定义一系列树操作和查询语言来解决数据访问和分析问题。

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.

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.