{"ID":2844196,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.07657","arxiv_id":"2511.07657","title":"CAE: Character-Level Autoencoder for Non-Semantic Relational Data Grouping","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.","short_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 seman...","url_abs":"https://arxiv.org/abs/2511.07657","url_pdf":"https://arxiv.org/pdf/2511.07657v1","authors":"[\"Veera V S Bhargav Nunna\",\"Shinae Kang\",\"Zheyuan Zhou\",\"Virginia Wang\",\"Sucharitha Boinapally\",\"Michael Foley\"]","published":"2025-11-10T22:07:22Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}