Queryable Self-Organizing Maps: A Database Abstraction for Topology-Driven Data Exploration
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 l...
| Authors | Denis Mayr Lima Martins, Gottfried Vossen |
|---|---|
| Primary category | cs.DB |
| Published | 24 July 2026 |
| Last updated at source | 24 July 2026 |
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