Dataset Content Embedding for Scalable Latent-Index Search
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Solution Overview
Problem
Existing database search systems face inefficiencies due to large data volumes, complex relationships, and poorly designed queries, leading to slow search times, especially when hardware resources are limited.
Innovation Solution
Generating a latent index based on metadata using an embedding algorithm, which reduces the data volume to be searched and preserves complex relationships, and creating an embedded request compatible with the latent index to facilitate efficient search operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If the database contains a significant amount of data, then the data storage capacity is improved, but the search operation time increases
Solution Approach 1:
The patent segments the large database into a latent index (compressed representation) and the original data. The latent index stores embedded representations of data records, allowing rapid searching without processing the entire large dataset. This segmentation resolves the contradiction by enabling fast searches on the segmented index while the full data remains stored for retrieval.
Solution Approach 2:
The patent performs preliminary action by pre-computing the latent index from the database data before search operations. The embedding algorithm processes the entire dataset in advance to create the compressed latent index structure, so that during search operations, the system only needs to query the pre-processed index rather than scanning the full data volume, thus reducing search time.
2Adaptability or versatility
If complex relationships and joins are included in the database, then the data modeling capability is improved, but the query execution time increases
Solution Approach 1:
The patent introduces the latent index as an intermediary structure between the complex database relationships and the search queries. The embedding algorithm processes complex joins and relationships during index construction, encoding them into the latent index structure. During searches, the intermediary latent index handles the complexity, allowing simple queries to retrieve results without directly processing complex relationships, thus reducing query execution time while preserving modeling capability.
3Quantity of substance
If the database server has limited hardware resources, then the system cost is reduced, but the ability to handle complex queries decreases
Solution Approach 1:
The patent creates a compressed copy of the database in the form of a latent index. Instead of requiring the database server to process all original data with complex relationships, the system uses the latent index (a compact copy containing essential information) for search operations. This copying approach allows limited hardware resources to handle queries efficiently while the full data capability is preserved in the index structure.
Data Source
AI summary
Methods and systems for scalable dataset content embedding for improved searchability. For example, the system may retrieve a first dataset from a first data source. The system may generate a first data profile of the first dataset. The system may generate a latent index of the first data profile based on processing the first data profile using a first embedding algorithm. The system may receive, via a user interface, a first request for a first text string. The system may generate an embedded request corresponding to the first request based on processing the first text string using the first embedding algorithm. The system may process the embedded request using the latent index. The system may generate for display, in the user interface, a result based on processing the embedded request using the latent index.