Graphical Relationship Map Generation for Heterogeneous Data Integration
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Solution Overview
Problem
Existing systems face challenges in efficiently determining and visualizing relationships across multiple information systems due to differing formatting, interfaces, and security protocols, requiring numerous user inputs and being prone to informational faults and errors.
Innovation Solution
A system that generates graphical relationship maps by linking structured and unstructured data sets using common keys, natural language processing, and entity types, reducing the need for multiple user inputs and enhancing data visualization across various environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to determine relationships between entities across multiple information systems, then user control and customization are maintained, but the process becomes time-consuming and requires numerous user inputs
Solution Approach 1:
The system performs automatic entity relationship determination without requiring user intervention. The processor autonomously retrieves data from multiple information systems, applies formatting rules, and generates relationship maps, allowing the system to serve itself rather than requiring manual user input for each relationship determination.
Solution Approach 2:
The system pre-retrieves and pre-formats data from multiple information systems before relationship determination is needed. By maintaining pre-formatted data sets with standardized entity definitions and relationship types, the system eliminates the need for real-time data processing when users query relationships, significantly reducing response time.
2Adaptability or versatility
If multiple information systems with differing formats and protocols are integrated manually, then system compatibility is achieved, but the complexity of integration increases
Solution Approach 1:
The system implements a universal data formatting approach that can handle multiple information systems with different protocols and formats. By using standardized entity definitions, relationship types, and formatting rules that apply across all data sources, the system achieves compatibility with diverse systems through a single unified methodology rather than requiring system-specific integration approaches.
Solution Approach 2:
The system introduces a intermediary processing layer that standardizes data from various information systems. This intermediary layer applies consistent formatting rules and entity definitions to transform heterogeneous data into a unified structure, mediating between diverse source systems and the relationship mapping functionality without requiring direct integration between each system pair.
3Reliability
If comprehensive data retrieval is performed across all information systems, then complete relationship information is obtained, but resource demands and error susceptibility increase
Solution Approach 1:
The system retrieves and processes only the specific data portions needed for the requested relationship query rather than comprehensively processing all available data. By selectively applying formatting rules and entity definitions relevant to the specific query, the system obtains sufficient relationship information with reduced data processing volume and lower resource consumption.
Data Source
AI summary
A system for generating graphical relationship maps is disclosed. The system may receive a search request. The system may generate a relationship map data based on linked data elements of at least one of a structured data set, an unstructured data set, and a hybrid data set comprising structured and unstructured data. The system may display a graphical relationship map including an entity type icon based on the relationship map data and the search request.