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LLMs applied to industrial data mapping and relationship discovery · 2 papers

By PulseAugur Editorial · [2 sources] ·

Two new research papers explore the application of large language models (LLMs) in industrial data management. The first paper, ColRel, focuses on discovering relationships within data lakes, particularly for ERP-derived datasets with complex schema labels, by building column embeddings from metadata and data. The second paper introduces Constraint-Guided Mapping (CGM), a neuro-symbolic method that uses admissibility constraints to improve enterprise entity alignment, ensuring structural and physical invariants are maintained while reducing expert effort. AI

IMPACT These papers demonstrate advanced applications of LLMs in complex data integration and relationship discovery, potentially improving efficiency and accuracy in enterprise data management.

RANK_REASON Two academic papers published on arXiv detailing novel methods for data management using LLMs.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLMs applied to industrial data mapping and relationship discovery · 2 papers

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Two academic papers published on arXiv detailing novel methods for data management using LLMs.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Ahlame Diouan (ERIC, UL2), Eric Ferey (ERIC, UL2), Sabine Loudcher (ERIC, UL2), J\'er\^ome Darmont (ERIC, UL2) ·

    Discovering Relationships in Data Lakes Using Large Language Models: An Industrial Case

    arXiv:2608.26750v1 Announce Type: new Abstract: Data lakes rely on metadata to remain usable, yet this meta data is often limited or weakly informative for column relationship discovery, especially in ERP-derived datasets with coded or abbreviated schema labels. We propose ColRel…

  2. arXiv cs.AI TIER_1 English(EN) · Sebastian Monka, Pramod Anantharam, Thien Vo Minh, Lavdim Halilaj ·

    Constraint-Guided Enterprise Data Mapping with Large Language Models

    arXiv:2608.24218v1 Announce Type: new Abstract: Enterprise entity alignment must handle semi-structured records, implicit attributes, and unit or granularity mismatches. Manual matching is still common in practice, but does not scale as schemas and providers evolve. LLM-only matc…

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