database querying

This study investigates how domain-specific metadata schemas can be effectively integrated with the generic DataCite schema to enhance metadata quality and interoperability in research data repositories. Through structural comparisons, cross-schema mapping analyses, and workflow evaluations of metadata records from eight repositories in the earth and social sciences, the research reveals how disciplinary characteristics influence the completeness of DataCite records. Findings indicate that discrepancies between schemas stem primarily from differing modeling philosophies rather than expressive capacity. While optimized cross-schema mappings significantly improve metadata quality, the diversity of repository workflows also critically affects record completeness. Building on these insights, the study proposes a strategy that leverages the complementary strengths of domain-specific and generic schemas, offering practical guidance for fostering interdisciplinary data sharing.

This work addresses the longstanding tension between usability and expressiveness in graph database analysis tools: conventional business intelligence systems lack native graph reasoning capabilities, while specialized query languages impose steep learning curves and fragment analytical workflows. To bridge this gap, we propose GPQL—a formal, composable, and cross-database-compatible graph query language—and introduce the first no-code visual analytics system that automatically compiles user interactions into valid GPQL queries. By integrating visualization designs centered on graph patterns and relationships, our system substantially lowers the barrier for non-technical users to conduct sophisticated graph analyses. Through a 22-month mixed-methods study in telecommunications and supply chain domains—including MILC-based evaluation—we demonstrate that our approach effectively supports real-world graph exploration workflows employed by professional analysts.

This work proposes a novel dataset discovery framework that leverages citation contexts from scientific papers to better capture the semantic intent behind research queries, addressing the limitations of existing dataset search engines that rely primarily on metadata and keyword matching and consequently suffer from low recall. By treating citation context as the core signal—combined with large-scale context extraction, large language model–guided pattern recognition, and provenance-preserving entity resolution—the approach significantly reduces dependence on incomplete or inconsistent metadata. Evaluated on eight computer science queries, the method achieves an average normalized recall of 47.47% (peaking at 81.82%), substantially outperforming Google Dataset Search and DataCite Commons. The framework’s novelty and practical utility have been affirmed by domain experts across multiple disciplines.

Data analysts face two primary bottlenecks: SQL generation and visualization selection. Existing approaches exhibit significant limitations in comprehending complex schemas, modeling ambiguous user intents, generalizing across domains, and enabling end-to-end text-to-visualization translation. This paper introduces TiInsight, a domain-agnostic system for automated exploratory data analysis (EDA). Its core contributions are: (1) Hierarchical Data Context (HDC) modeling, which enhances large language models (e.g., GPT-4) to reason over heterogeneous schemas and imprecise user intents; and (2) an end-to-end four-stage EDA pipeline—intent clarification, TiSQL (text-to-SQL), TiChart (automated chart recommendation), and GUI integration. TiSQL achieves 86.3% execution accuracy on Spider and sets a new state-of-the-art on Bird; user studies demonstrate superior performance over human experts. The system’s API is open-sourced and deployed in PingCAP’s production environment.

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.

This work proposes ExBI, a novel exploratory business intelligence system that overcomes the limitations of traditional BI platforms—such as rigid schemas, high computational overhead, and reliance on expert knowledge—by introducing a hypergraph data model. ExBI features specialized Source, Join, and View operators to enable dynamic schema evolution and materialized view reuse, while integrating a sampling-based estimation algorithm with theoretical error guarantees for efficient and accurate multi-round querying. Experimental evaluation on the LDBC benchmark demonstrates that ExBI achieves an average speedup of 16.21× (up to 146.25×) over Neo4j and 46.67× (up to 230.53×) over MySQL, with an average error rate of only 0.27% for COUNT queries.

This study addresses the lack of systematic empirical analysis on the adoption and evolution of database management systems (DBMSs) in open-source projects. By examining the code history of 362 popular GitHub Java repositories, the work combines source-code heuristics, DB-Engines rankings, ORM detection, and version tracking to uncover long-term DBMS evolution patterns. The findings reveal that MySQL and PostgreSQL are the most prevalent relational DBMSs, while Redis and MongoDB exhibit stable usage among non-relational systems. HyperSQL is frequently replaced, and a “polyglot persistence” pattern—characterized by coexistence and cross-type collaboration of multiple DBMSs—is widespread. Moreover, distinct DBMSs demonstrate significantly different propensities for replacement, highlighting nuanced evolutionary dynamics in real-world software ecosystems.

This work proposes the first unified, scalable, and domain-agnostic automated framework to address the time-consuming and error-prone nature of manual collection and curation of scientific literature. By integrating keyword-based retrieval, parallel harvesting from multiple API sources, and domain-specific filtering via customized large language model (LLM) prompts, the framework efficiently constructs open scientific databases. Evaluated on agricultural and crop yield tasks, the generated database achieves a 90% overlap with a small expert-curated benchmark, demonstrating high fidelity while substantially reducing manual effort. These results validate the framework’s effectiveness and practical utility for accelerating open science initiatives across diverse research domains.

Enterprise text-to-SQL systems often fail due to incorrect database schema context, particularly in large-scale data warehouses characterized by complex table structures, informal metrics, implicit relationships, and access constraints. This work proposes Schema-First Retrieval, which formulates catalog selection as a standalone semantic retrieval problem. It introduces type-aware templated embeddings to encode tables, columns, metrics, relationships, and query history, and integrates vector retrieval, lineage expansion, cross-encoder reranking, workload memory, and rule-based access control into a multi-stage retrieval architecture. Experiments show that the method achieves 96.4% table recall@20 on CRUSH4SQL, improves column recall@10 by 11.1 points, outperforms BM25 by 32.8 points in table recall@5, boosts table recall@5 on SEDE from 52.1% to 92.3% using query history, and reduces SQL execution error rates by 2.5× on BIRD.

This work addresses the significant limitations of spreadsheet-based analysis in reproducibility, auditability, version control, and automation. It proposes a migration pathway from Excel to research-grade analytical workflows by leveraging Python’s pandas library as a bridge. The study introduces an innovative set of Excel-to-pandas mapping rules, categorizes nine canonical workflow patterns, and compiles a catalog of common failure modes. Seven end-to-end real-world examples demonstrate the approach in practice. By retaining Excel as a familiar interface for input and output while integrating version control, automated refreshing, and seamless incorporation of statistical and machine learning methods, the proposed framework enables governed, reproducible, and auditable tabular data analysis.