Information Retrieval Framework for Personal Data
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Solution Overview
Problem
Current information retrieval frameworks face challenges in efficiently managing and retrieving personal data while maintaining security and privacy, including issues with model identification, manual adjustments, scalability, and consistency, leading to inaccuracies and increased complexity.
Innovation Solution
The framework represents data models as code, using business add-ins and code extensions to generate and manage models with defined boundaries for each information lifecycle management object, allowing for accurate data retrieval and reducing manual errors and complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual adjustments are made to data models, then model identification and consistency can be achieved, but device complexity and time consumption increase
Solution Approach 1:
The system automatically generates data models by self-service mechanisms including automated code generation from data definitions, automatic population of model boundaries, and automated linkage creation between data objects, eliminating the need for manual model identification and adjustment while maintaining accuracy
Solution Approach 2:
The framework performs preliminary actions by pre-defining model boundaries during data model creation, pre-establishing linkage relationships between data objects, and pre-generating code structures, so that when data is retrieved, the models are already identified and consistent without requiring manual intervention
2Loss of information
If comprehensive data retrieval is performed across distributed databases, then complete personal data can be provided, but security and privacy of other data may be compromised
Solution Approach 1:
The system segments data access by creating distinct data models with defined boundaries for each data object, allowing retrieval operations to be scoped to specific models rather than sweeping across all distributed databases, thus obtaining complete personal data for the target individual while leaving other data segments untouched and secure
Solution Approach 2:
The framework introduces data models as intermediary layers between the retrieval system and distributed databases. These models act as mediators that define precise boundaries and relationships, enabling the system to query only the necessary data within model boundaries while maintaining security controls and privacy protections for data outside those boundaries
3Productivity
If automated model generation is implemented, then productivity and scalability improve, but manufacturing precision of model boundaries may decrease
Solution Approach 1:
The system incorporates feedback mechanisms where generated data models are validated against predefined criteria and requirements, automatic corrections are applied to refine model boundaries, and the generation process iterates until precision thresholds are met, ensuring that automated generation maintains high accuracy in model boundary definition
Data Source
AI summary
Systems and processes for managing an information retrieval database are provided. In a method for modeling a data object storing table relationships for tables belonging to a computer application, a first set of table links are retrieved from the data object and an enhanced set of table links are generated by appending additional table links to the first set based on linkages within the tables and/or code extensions/add-ins. A first set of field links are generated for the data object by matching metadata of the enhanced set of table links, and an enhanced set of field links are generated by performing additions, deletions, or replacements within the first set based on characteristics of the field links in the first set and/or code extensions/add-ins. A model for the data object is generated, representing the enhanced set of table links and the enhanced set of field links, and stored in a database.