Abstract
Traditionally, data is stored in an addressable manner and is retrieved using database queries. Even when compression techniques are utilized, such data storage incurs a substantial cost and requires technical database skills to retrieve. This disclosure describes artificial intelligence (AI) techniques to efficiently store data. The techniques leverage the ability of AI models to ingest and compress data during a training phase and to reconstruct that data during a prediction phase. An AI model, thus repurposed for storage, automatically compresses the data by storing the data within model parameters. The stored data can be recovered using natural language queries that require no technical skill. A query can include filter-by-view phrases, such that data analysis becomes part of data retrieval, and is handled by the AI model during data reconstruction, thereby obviating the need for dedicated data analysis tools.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
NA, "Data Storage Using Artificial Intelligence Models", Technical Disclosure Commons, (August 22, 2024)
https://www.tdcommons.org/dpubs_series/7298