让自组织映射在数据库内可查询，实现拓扑驱动的数据探索。
Queryable Self-Organizing Maps: A Database Abstraction for Topology-Driven Data Exploration
- 将SOM拓扑作为可查询的数据库对象，保留代表点与邻接关系。
- 在中等规模数据上训练可行，映射查询响应迅速，支持交互式探索。
- 适合需要发现数据分布结构的研究者或数据分析师。
自组织映射（SOM）长期用于高维数据的探索性分析：它将数据对象组织成二维拓扑，揭示聚类、梯度、稀疏/密集区域及边界。然而，在现代数据系统中，SOM通常在数据库外训练与可视化，与所总结的关联数据脱节。本文提出可查询数据地图的抽象：一种包含代表点、邻域关系、对象分配及衍生摘要的可学习拓扑结构。我们通过轻量级原型MapDB实现了该思想，使SOM成果可在数据库内直接查询，用户无需离开数据库即可探索数据拓扑。实验表明，在中等分析规模下训练可行，映射物化后查询可交互，且SOM区域能为探索性SQL提供有意义的目标。
原文摘要 · Abstract (English)
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.
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