Smart Dataset Collection System Metadata Layer
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Solution Overview
Problem
Existing dataset management systems face challenges in providing versioning, quality control, metadata generation, linking, and summarization, leading to difficulties in accessing and utilizing high-quality datasets across enterprise environments, where datasets are often siloed and lack well-defined metadata, resulting in inefficient search processes.
Innovation Solution
The implementation of a system that generates and stores metadata for datasets, including versioning, quality scores, topics, and semantic relatedness scores, allowing for systematic dataset management, search functionality by topic and quality score, and natural language summarization to facilitate easy human-readable summaries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If datasets are stored in silos without centralized metadata management, then data sovereignty and source integrity are maintained, but search efficiency and dataset accessibility deteriorate
Solution Approach 1:
The patent introduces a metadata layer as an intermediary between the distributed dataset sources and the search functionality. This metadata layer centralizes information about datasets (including quality scores, versioning, and descriptions) without centralizing the actual data storage, thus maintaining data integrity while enabling efficient centralized search and access.
2Measurement precision
If comprehensive metadata including quality scores and versioning is generated for all datasets, then dataset quality control and identification are improved, but system complexity and resource usage increase
Solution Approach 1:
The system performs preliminary actions by automatically generating metadata, quality scores, and versioning information during data ingestion and processing stages. This preliminary metadata generation enables efficient search and quality assessment without requiring complex real-time analysis, thus improving measurement precision while managing system complexity.
Solution Approach 2:
The metadata generation system operates autonomously, automatically creating and updating metadata, quality scores, and versioning information without requiring manual intervention. This self-service approach improves quality assessment accuracy while minimizing the operational complexity and resource burden on users.
3Ease of operation
If natural language summaries and semantic relatedness scores are generated for datasets, then ease of identification and searchability are improved, but computational resources and processing time are consumed
Solution Approach 1:
Natural language summaries and semantic relatedness scores are generated in advance during data processing and metadata creation stages. This preliminary generation of semantic information enables fast, resource-efficient search and identification operations, as the computationally intensive natural language processing is performed once rather than repeatedly during query operations.
Data Source
AI summary
Datasets are available from different dataset servers and often lack well-defined metadata. Thus, comparing datasets is difficult. Additionally, there might be different versions of the same dataset which makes the search even more difficult. Using systems and methods described herein, quality scores, dataset versioning, topic identification, and semantic relatedness metadata is stored about datasets stored on dataset servers. A user interface is provided to allow a user to search for datasets by specifying search criteria (e.g., a topic and a minimum quality score) and to be informed of responsive datasets. The user interface may further inform the user of the quality scores of the responsive datasets, the versions of the responsive datasets, or other metadata. From the search results, the user may select and download one or more of the responsive datasets.