Database Metadata Mapping for Personal Information Location
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Solution Overview
Problem
Existing data storage and management solutions struggle to efficiently and accurately locate personal information across disparate databases due to varying database sizes, types, locations, and security measures, making it difficult to comply with privacy legislation requirements for quick data retrieval.
Innovation Solution
A computing device with collector modules communicates with multiple databases, retrieves metadata without collecting data, aggregates and standardizes it into a common format, applies database metadata rules to identify patterns indicative of personal information, and generates a database metadata table to determine the location of PI-associated data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing data storage solutions are used to store personal information across numerous databases, then data capacity and storage flexibility are improved, but data location efficiency and accuracy deteriorate
Solution Approach 1:
The system performs preliminary actions by collecting and analyzing database metadata before actual data retrieval operations. The metadata collection module proactively gathers information about database structure, location, and content characteristics, creating a preparatory knowledge base that enables efficient subsequent data location without requiring full data scanning
Solution Approach 2:
Database metadata serves as an intermediary between the storage system and query system. Instead of directly searching through vast amounts of stored personal information across numerous databases, the system uses metadata as a mediating layer that provides summarized information about data locations, structures, and characteristics, dramatically reducing the complexity of data location operations
2Loss of information
If comprehensive data collection is performed across disparate databases, then data completeness is improved, but processing time and computational resources worsen
Solution Approach 1:
The system extracts only the essential metadata information from databases rather than collecting complete data sets. By taking out just the necessary structural and descriptive information needed to locate and understand personal information, the system achieves sufficient data completeness for location purposes while avoiding the time-consuming process of handling full data contents
Solution Approach 2:
The system applies partial action by collecting metadata that is sufficient (but not excessive) for data location purposes. Rather than performing complete data analysis or retrieving all possible information, the system gathers the specific metadata portions needed to efficiently locate personal information, avoiding unnecessary processing overhead
3Adaptability or versatility
If metadata from multiple database types is aggregated, then data coverage across databases is improved, but metadata standardization complexity worsens
Solution Approach 1:
The system implements a universal metadata standard that can represent multiple database types and structures through a common format. This universal metadata schema is designed to accommodate diverse database sources (relational, NoSQL, hierarchical) while maintaining a consistent structure that simplifies aggregation and analysis, allowing the same metadata framework to handle various database technologies
4Measurement precision
If PI identification rules are applied to locate personal information, then identification accuracy is improved, but false positive rate worsens
Solution Approach 1:
The system implements feedback mechanisms in its PI identification process by analyzing metadata patterns and learning from identification results. The system uses the structured metadata information to refine its understanding of what constitutes personal information in different contexts, adjusting its identification approach based on feedback from previous analyses to reduce false positives while maintaining accuracy
Data Source
AI summary
Methods, systems, and apparatuses for improved data storage and data management are described herein. These methods, systems, and apparatuses may efficiently and accurately locate data associated with personal information (PI) within a single database as well as across a large data storage network consisting of numerous, disparate data stores. As an example, a computing device may use a database metadata table to determine a location(s) of PI-associated data across a plurality of databases.