Automated Database Ownership Attribution via Bi-Partite Graphs
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Solution Overview
Problem
In large organizations with data lakes, determining the owner of database tables for management and access control is challenging due to the lack of ownership information, leading to manual, resource-intensive processes that delay organizational workflows and compromise data security.
Innovation Solution
An automated system creates a queryable bi-partite graph by establishing connections between users and database tables based on user commands, assigning scores to these connections, and storing them for real-time ownership attribution, enhancing security and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual processes are used to identify database table owners, then ownership information can be obtained, but organizational workflow is delayed and resources are wasted
Solution Approach 1:
The system performs preliminary action by automatically capturing and storing ownership information at the time of database table creation. The ownership attribution system proactively identifies owners based on user commands and database operations before any manual lookup is needed, making ownership information readily available when required without delaying workflows.
Solution Approach 2:
The patent replaces manual mechanical processes (Excel document tracking, manual contact with managers) with an automated computer-based system. The ownership attribution system uses algorithms to analyze user commands, database operations, and access patterns to automatically determine and attribute ownership, eliminating the need for manual intervention and significantly improving organizational workflow efficiency.
2Ease of operation
If open access to database tables is provided to all members, then accessibility is improved, but data security is compromised
Solution Approach 1:
The ownership attribution system introduces an intermediary layer between database members and database tables. Instead of providing direct open access or restricting all access, the system automatically identifies the owner of each database table and enables appropriate access control decisions. This intermediary ownership information allows the organization to implement the principle of least privilege access while maintaining ease of operation for authorized users.
Solution Approach 2:
The system implements feedback by continuously monitoring user commands, database operations, and access patterns to dynamically attribute ownership. This feedback mechanism ensures that ownership information remains current and accurate, enabling real-time access control decisions that balance accessibility and security based on actual usage patterns rather than static permissions.
3Loss of information
If conventional manual techniques are used for ownership attribution, then ownership identity can be determined, but severe organizational workflow bottlenecks are created
Solution Approach 1:
The patent replaces manual mechanical processes (Excel tracking, manual contact with managers) with an automated computer-based system. The ownership attribution system uses algorithms to analyze user commands, database operations, and access patterns to automatically determine and attribute ownership, eliminating the need for manual intervention and significantly reducing the time required to identify database table owners across thousands to millions of tables.
Solution Approach 2:
The ownership attribution system performs self-service by automatically identifying and attributing ownership without requiring manual intervention. The system monitors database operations and user commands autonomously, using algorithms to determine ownership based on creation patterns, access patterns, and operational history, thereby eliminating workflow bottlenecks caused by manual processes.
Data Source
AI summary
Systems and methods for automated techniques that generate queryable database table ownership attribution information in real-time. In addition to generating ownership attribution information, system and methods provide a novel framework for creating bi-partite graphs and generating insightful graph data.