Designing a Visual Tool for Property Graph Schema Extraction and Refinement: An Expert Study

MORTAL: A Tool of Automatically Designing Relational Storage Schemas for Multi-model Data through Reinforcement Learning

Considering relational databases having powerful capabilities in handling security, user authentication, query optimization, etc., several commercial and academic frameworks reuse relational databases to store and query semi-structured data (e.g., XML, JSON) or graph data (e.g., RDF, property graph). However, these works concentrate on managing one of the above data models with RDBMSs. That is, it does not exploit the underlying tools to automatically generate the relational schema for storing multi-model data. In this demonstration, we present a novel reinforcement learning-based tool called …

面向智能体的模式精炼

Towards Agentic Schema Refinement

针对大型企业数据库复杂混乱、掩盖分析所需数据语义的问题，研究提出由易解读小型数据库视图构成的语义层作为精炼模式，并引入多智能体LLM模拟以极少输入迭代定义精炼视图，为LLM探索繁杂数据库铺路。

EGREFINE: 一种基于执行的文本到SQL模式精炼优化框架

EGREFINE: An Execution-Grounded Optimization Framework for Text-to-SQL Schema Refinement

本文提出EGRefine框架，通过约束优化问题解决文本到SQL模式精炼问题，采用列级贪心分解策略，确保模式精炼的安全性和查询等价性，提升模型准确性。

A Principled Approach to Bridging the Gap between Graph Data and their Schemas

Although RDF graphs have schema information associated with them, in practice it is very common to find cases in which data do not fully conform to their schema. A prominent example of this is DBpedia, which is RDF data extracted from Wikipedia, a publicly editable source of information. In such situations, it becomes interesting to study the structural properties of the actual data, because the schema gives an incomplete description of the organization of a dataset. In this paper we have approached the study of the structuredness of an RDF graph in a principled way: we propose a framework for…

DBAutoDoc: 通过统计分析和迭代LLM细化实现未记录数据库模式的自动发现与文档化

DBAutoDoc: Automated Discovery and Documentation of Undocumented Database Schemas via Statistical Analysis and Iterative LLM Refinement

DBAutoDoc通过统计分析与迭代LLM细化技术，自动发现并文档化未记录的关系数据库模式，其核心是通过图结构问题迭代解决，结合神经网络反向传播的结构，实现语义校正，最终在基准数据库上取得96.1%的综合评分。

OLxPBench: Real-time, Semantically Consistent, and Domain-specific are Essential in Benchmarking, Designing, and Implementing HTAP Systems

As real-time analysis of the new data become increasingly compelling, more organizations deploy Hybrid Transactional/Analytical Processing (HTAP) systems to support real-time queries on data recently generated by online transaction processing. This paper argues that real-time queries, semantically consistent schema, and domain-specific workloads are essential in benchmarking, designing, and implementing HTAP systems. However, most state-of-the-art and state-of-the-practice benchmarks ignore those critical factors. Hence, they are incommensurable and, at worst, misleading in benchmarking, desig…