MUMBAI, India, July 24 -- Intellectual Property India has published a patent application (202644085588 A) filed by Anjanadevi S C; Dr. Sumithra Devi K A; Dayananda Sagar Academy Of Technology And; Gajendra S; Mariam; Anusha; T. Poornima; and Shravani E S on July 13, 2026, for Intelligent Data Element Relationship Discovery System For Automatic Dataset Relationship Identification Without Reference Keys.
Inventors include Anjanadevi S C; Dr. Sumithra Devi K A; Dayananda Sagar Academy Of Technology And; Gajendra S; Mariam; Anusha; T. Poornima; and Shravani E S.
The application for the patent was published on July 17, 2026, under issue no. 29/2026.
Abstract: Data element relationship discovery plays a crucial role in modern data integration, schema matching, and metadata management systems, where identifying hidden relationships between datasets is essential for effective data analysis and interoperability. Traditional approaches mainly rely on predefined primary keys and foreign key constraints to establish relationships between tables. However, in many real-world scenarios, datasets may contain incomplete schemas, missing reference keys, heterogeneous column names, and inconsistent data formats, making conventional relationship detection methods ineffective. This project introduces an intelligent Data Element Relationship Discovery System that automatically identifies related datasets and discovers potential column- level relationships without using predefined reference keys. The proposed system utilizes standard benchmark datasets such as Transaction Processing Performance Council TPC-H and TPC-DS schemas to evaluate relationship discovery accuracy and robustness. The framework performs schema preprocessing, metadata extraction, data profiling, similarity computation, and semantic matching to detect hidden associations among tables and attributes. Multiple similarity measures including name-based similarity, datatype compatibility, statistical correlation, and content-based matching are integrated to improve relationship identification accuracy. The system successfully discovers implicit relationships between datasets, enabling efficient schema integration and intelligent metadata analysis. Experimental evaluation on benchmark schemas demonstrates that the proposed approach can accurately identify related tables and connecting attributes even in the absence of explicit foreign key information, thereby improving automated data integration and reducing manual schema mapping efforts.
Disclaimer: Curated by HT Syndication.
