AI-native systems are becoming core components of modern data-centric infrastructures, where artificial intelligence is embedded directly into data access, processing, and interpretation workflows. Despite advances in large language models, most research focuses on natural language to SQL generation, while the inverse...
Experiments on three real-world datasets demonstrate that AnnoIndex consistently outperforms state-of-the-art baselines, achieving the highest average F1 score while maintaining robust performance on complex multi-hop join and progressive reasoning queries.
Industrial automation systems generate large volumes of time-series data that are typically stored in relational databases. Accessing this data using Structured Query Language (SQL) requires technical expertise, limiting usability for non-expert users. This paper presents a comparative performance evaluation of machine...
Controlling database systems using traditional interfaces requires a strong understanding of Structured Query Language (SQL) and the database administration tools employed. This study introduces a new AI-based system that automates create, read, update, and delete database actions through natural language processing, s...
The Natural Language to SQL Query Generator is an intelligent system designed to allow users to retrieve information from databases by asking questions in simple natural language. Traditionally, users need knowledge of SQL syntax to interact with relational databases. This creates difficulties for non-technical users w...
Non-relational tables, such as spreadsheets and web tables, are widely used to store structured data, yet they are primarily designed for human reading rather than machine-driven analysis. These tables often contain hierarchical headers, merged cells, and implicit spatial semantics, making it difficult for large langua...
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