发表机构
University of Sao Paulo; University of Münster（圣保罗大学; 明斯特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对现代数据系统中SOM与关系数据脱节问题，引入可查询数据映射概念，通过MapDB原型实现，使SOM工件可查询，经实验验证其在适度规模下训练可行、查询具交互性且为探索性SQL提供有意义目标。
AI 中文摘要
自组织映射（SOM）长期以来一直用作高维数据的探索工具，可将对象组织成二维拓扑以揭示聚类、梯度等信息。但在现代数据系统中，SOM通常在数据库管理系统之外进行训练和可视化，与所总结的关系数据脱节。我们引入了可查询数据映射的抽象概念，通过轻量级原型MapDB实现，让用户无需离开数据库就能探索数据拓扑。实验表明，SOM训练在适度分析规模下可行，映射查询在实例化后具有交互性，SOM区域为探索性SQL提供了有意义的目标。
英文摘要
Self-Organizing Maps (SOMs) have long been used as exploratory tools for high-dimensional data: they organize objects into a two-dimensional topology that reveals clusters, gradients, sparse regions, dense regions, and boundaries. Yet, in modern data systems, SOMs are typically trained and visualized outside the DBMS, disconnected from the relational data they summarize. We introduce the abstraction of a queryable data map: a learned topological artifact consisting of representatives, neighborhood relations, object assignments, and derived summaries. We instantiate this idea with MapDB, a lightweight prototype that makes SOM artifacts queryable so users can explore data topology without leaving the database. Experimental study shows that SOM training is feasible at moderate analytical scale, that map queries are interactive after materialization, and that SOM regions provide meaningful targets for exploratory SQL.
