Knowledge Graph for Business Intelligence Data Visualization
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Solution Overview
Problem
Business intelligence systems face challenges in providing users with intuitive and insightful visual representations of data, as existing technologies struggle to efficiently identify and present relevant dimensions and correlations, leading to difficulties in anomaly detection and trend prediction.
Innovation Solution
The method involves using a knowledge graph to identify and select nodes with strong correlations, determining new values to maintain correlation while achieving target values, and generating visual data for user interfaces, thereby enhancing data analysis and insights generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data visualization methods are used, then implementation is simple, but the ability to provide deep insights and detect anomalies is insufficient
Solution Approach 1:
The patent introduces a knowledge graph as an intermediary layer between raw data and visualization. The knowledge graph stores pre-computed relationships, correlations, and contextual information about data elements, allowing the system to quickly retrieve meaningful insights without complex real-time analysis. This mediator enables deep insights while maintaining system manageability.
Solution Approach 2:
The system performs preliminary analysis by pre-computing correlations, relationships, and contextual information between data elements and storing them in the knowledge graph before visualization is needed. This advance preparation allows the visualization system to display deep insights immediately without performing complex calculations at render time, resolving the contradiction between insight depth and system complexity.
2Measurement precision
If comprehensive data analysis is performed to provide deep insights, then measurement precision improves, but processing time increases
Solution Approach 1:
The knowledge graph pre-computes and stores correlations, relationships, and contextual information between data elements in advance. When generating visualizations, the system retrieves this pre-analyzed information from the knowledge graph rather than performing comprehensive data analysis at render time, thereby achieving accurate pattern detection without excessive processing time.
Solution Approach 2:
The system applies different levels of analysis depth to different data elements based on their importance and the specific visualization context. The knowledge graph stores varying degrees of pre-computed information for different data elements, allowing the system to retrieve only the necessary level of analysis for each element, optimizing both accuracy and performance.
3Loss of information
If more dimensions and correlations are analyzed, then insights quality improves, but computational complexity increases
Solution Approach 1:
The knowledge graph serves as an intermediary that pre-processes and organizes complex multi-dimensional relationships and correlations. By storing these relationships in a structured format in the knowledge graph, the system can retrieve comprehensive contextual information without performing complex computational operations at visualization time, thus maintaining context while reducing computational burden.
Data Source
AI summary
A system and method for providing visual data for user interfaces based on a knowledge graph. A method includes identifying at least one second node with respect to a first node based on connections between nodes of a knowledge graph, wherein the knowledge graph includes the first node and the at least one second node, wherein the first node represents a dimension of interest; selecting at least one third node from among the at least one second node by determining a correlation between the first node and each of the at least one second node; determining a new value for a dimension of each of the at least one third node based on a target value such that the correlation of the third node to the first node is maintained while achieving the target value; and generating visual data for an action item user interface based on the new values.