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

• URL: http://arxiv.org/abs/2511.07657v1
• Date: Wed, 12 Nov 2025 01:09:53 GMT
• Title: CAE: Character-Level Autoencoder for Non-Semantic Relational Data Grouping
• Authors: Veera V S Bhargav Nunna, Shinae Kang, Zheyuan Zhou, Virginia Wang, Sucharitha Boinapally, Michael Foley,
• Abstract summary: This paper introduces a novel Character-Level Autoencoder (CAE) approach that automatically identifies and groups semantically identical columns in non-semantic relational datasets.<n>Unlike conventional Natural Language Processing (NLP) models that struggle with limitations in semantic interpretability, our approach operates at the character level with fixed dictionary constraints.<n>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.
• Score: 0.9595254895337946
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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