CAE: Character-Level Autoencoder for Non-Semantic Relational Data Grouping

TL;DR

This paper presents a character-level autoencoder that effectively groups semantically identical columns in large-scale non-semantic relational datasets, overcoming NLP limitations and improving data profiling accuracy.

Contribution

The novel CAE approach operates at the character level with fixed dictionaries, enabling scalable, efficient identification of column similarities in industrial data environments.

Findings

Achieved 80.95% accuracy in top 5 column matching

Outperformed traditional NLP methods like Bag of Words (47.62%)

Reduced memory and training time for large datasets

Abstract

Enterprise relational databases increasingly contain vast amounts of non-semantic data - IP addresses, product identifiers, encoded keys, and timestamps - that challenge traditional semantic analysis. This paper introduces a novel Character-Level Autoencoder (CAE) approach that automatically identifies and groups semantically identical columns in non-semantic relational datasets by detecting column similarities based on data patterns and structures. Unlike conventional Natural Language Processing (NLP) models that struggle with limitations in semantic interpretability and out-of-vocabulary tokens, our approach operates at the character level with fixed dictionary constraints, enabling scalable processing of large-scale data lakes and warehouses. The CAE architecture encodes text representations of non-semantic relational table columns and extracts high-dimensional feature embeddings for data grouping. By maintaining a fixed dictionary size, our method significantly reduces both memory requirements and training time, enabling efficient processing of large-scale industrial data environments. Experimental evaluation demonstrates substantial performance gains: our CAE approach achieved 80.95% accuracy in top 5 column matching tasks across relational datasets, substantially outperforming traditional NLP approaches such as Bag of Words (47.62%). These results demonstrate its effectiveness for identifying and clustering identical columns in relational datasets. This work bridges the gap between theoretical advances in character-level neural architectures and practical enterprise data management challenges, providing an automated solution for schema understanding and data profiling of non-semantic industrial datasets at scale.