Graph Database for Identifying Access Patterns
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Solution Overview
Problem
Existing data storage systems often lack sufficient metadata to effectively identify all relevant data sources for keyword searches, limiting the ability to find information necessary for completing tasks.
Innovation Solution
A graph database is used to store data assets, including information about users, relationships, and usage patterns, allowing for keyword searches to identify relevant data structures by traversing nodes and edges, providing supplemental search results beyond primary keyword matches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional keyword search is used, then search speed is fast, but search completeness is insufficient
Solution Approach 1:
The system pre-computes and stores access patterns, user relationships, and data asset connections in a graph database before search requests arrive. This preliminary structuring of data relationships enables rapid traversal during search operations, achieving both completeness and speed
Solution Approach 2:
A graph database serves as an intermediary between traditional keyword search and comprehensive data discovery. It mediates by storing pre-analyzed relationships and access patterns, allowing the system to quickly retrieve relevant data assets without performing exhaustive searches
2Measurement precision
If comprehensive data analysis is performed, then search accuracy is improved, but system resource consumption increases
Solution Approach 1:
Access patterns, user relationships, and data asset connections are pre-computed and stored in the graph database during idle periods or data ingestion phases. This eliminates the need for real-time comprehensive analysis when search requests arrive, reducing resource consumption while maintaining high accuracy
Solution Approach 2:
The graph database automatically maintains and updates relationship structures as new data arrives, without requiring external analysis resources. The system serves itself by continuously enriching the graph structure with new access patterns and relationships
3Measurement precision
If more metadata is collected, then data source identification is improved, but data storage complexity increases
Solution Approach 1:
The system segments metadata collection into distinct relationship types (user-data associations, user-user relationships, data-access patterns) and stores them as separate edge types in the graph database. This segmentation makes the complex metadata structure manageable and queryable
Solution Approach 2:
The graph database structure acts as an intermediary layer that organizes complex metadata into standardized relationship types. Instead of storing raw, unstructured metadata, the system transforms it into structured graph relationships that are easier to manage and query
Data Source
AI summary
An information handling system for servicing search requests for information stored in data assets includes storage for storing a graph database based on at least one data catalog for the data assets. The information handling system also includes a processor that obtains, from a requestor, a search request of the search requests, the search request specifies a keyword search term; obtains a first identifier of a data asset of the data assets using the graph database and the keyword search term; and provides the first identifier as a supplemental result for the search request.