Data Information Framework for Personal Data Retrieval
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Solution Overview
Problem
Existing data management systems face challenges in identifying, retrieving, and reporting personal data due to the complexity of data storage and compliance with laws requiring data subjects to access their personal information in intelligible form.
Innovation Solution
A data information framework that creates a data model with links between tables using purpose information, allowing for the grouping of tables into clusters, and includes field descriptions for intelligible text rendering, enabling efficient data retrieval across systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is stored in complex data warehouses with multiple tables and archives, then data storage capacity and organization are improved, but data retrieval complexity and time increase
Solution Approach 1:
The patent creates a data information framework that pre-establishes links between data in different tables and archives before retrieval is needed. Purpose information is pre-assigned to data fields, and the framework pre-identifies where personal data is stored across the data warehouse, enabling fast retrieval without traversing complex table relationships at query time
Solution Approach 2:
The patent introduces an intermediary data information framework that sits between the complex data warehouse and the retrieval system. This framework contains purpose information and pre-defined links that mediate between the complex stored data and the simple retrieval query, translating complex data relationships into straightforward access paths
2Productivity
If data is stored across multiple tables and systems, then data organization and storage efficiency are improved, but identification and retrieval of related data become more difficult
Solution Approach 1:
The patent introduces purpose information as an intermediary layer that connects data across multiple tables and systems. This purpose information acts as a mediator that automatically identifies related data by matching purposes, eliminating the need for complex manual tracing of data relationships across distributed storage locations
Solution Approach 2:
The patent creates a universal data information framework that can handle multiple types of data relationships and purposes through a single unified structure. The framework uses standardized purpose information that can represent various data characteristics (personal data, legal hold, sensitive information) and automatically applies to data across different tables, systems, and storage locations
3Measurement precision
If purpose information is assigned to data fields to enable compliance reporting, then data retrieval accuracy is improved, but data model complexity increases
Solution Approach 1:
The patent applies purpose information at the local level of individual data fields rather than requiring complex global data models. Each data field can have its own purpose information assigned independently, allowing precise identification of personal data without creating intricate relationships between data elements. This localized approach maintains simplicity while achieving high retrieval accuracy
Data Source
AI summary
A data information framework collects related data sharing characteristics (e.g., personal information, others) revealed by associated purpose information, and reports on that data. The location of the data is not restricted, and can be collected from various locations (e.g. different databases on different computer systems). An engine implements data creation defining links between different stored data structures (e.g., tables) using specific fields. A plurality of tables may be grouped into a smaller number of table clusters to facilitate constructing the data model. The model may be evaluated, enhanced, and/or corrected (e.g., by a user). The model may include fields reflecting the purpose information for the stored data, said fields accessible by the engine during data handling processes. The data model may include descriptions providing data storage location. Purpose information may be mapped to table fields. Field descriptions may be based upon purpose information, with some field values having intelligible text.