Enterprise Dashboard Visualization Using Knowledge Graph Asset Insights
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional data analytics and digital transformation for industrial assets are inefficient due to the need for human interaction and difficulty in determining inter-relationships between data from multiple systems, leading to time-consuming issue identification and resolution.
Innovation Solution
A system utilizing a knowledge graph to correlate operational technology data and provide insights, enabling automated dashboard visualizations and adjustments to operational settings for industrial assets, supported by an AI-driven cognitive advisor for integrity operating window optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data analytics methods are used with human interaction, then insights can be provided, but the process is time-consuming and inefficient
Solution Approach 1:
The system enables automated self-service through the knowledge graph that automatically correlates operational technology data from multiple systems without human intervention. The cognitive advisor autonomously generates insights and recommends actions, eliminating the need for manual data analysis and significantly improving productivity while reducing time loss.
Solution Approach 2:
The patent replaces manual mechanical analysis processes with an automated knowledge graph system. The knowledge graph automatically correlates data from multiple systems using predefined relationships and rules, substituting human analysts with an automated computational system that processes data faster and more efficiently.
2Loss of information
If data from multiple systems is analyzed, then comprehensive insights are obtained, but determining inter-relationships becomes difficult and time-consuming
Solution Approach 1:
The knowledge graph serves as an intermediary layer between multiple operational technology systems. It standardizes data from diverse sources into a unified structure with predefined relationships, making it easier to correlate data without directly managing the complexity of inter-system relationships. The cognitive advisor then queries this standardized structure to generate comprehensive insights.
3Reliability
If more assets are monitored by a specialized worker, then better asset management is achieved, but the difficulty of identifying and fixing issues increases
Solution Approach 1:
The knowledge graph system provides self-service monitoring capabilities that automatically detect and correlate issues across multiple assets without requiring specialized workers to manually analyze each asset. The system autonomously identifies patterns and relationships, making it scalable to monitor large numbers of assets while maintaining high reliability and reducing the difficulty of issue detection.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Various embodiments described herein relate to an enterprise data management dashboard. In this regard, a request to generate a dashboard visualization related to one or more assets is received. The request includes an asset descriptor describing the one or more assets. In response to the request, aspects of aggregated operational technology data within a knowledge graph data structure are correlated to provide one or more insights associated with the one or more assets. Additionally, the dashboard visualization is provided to an electronic interface of a computing device. The dashboard visualization includes visualization data for the one or more insights associated with the knowledge graph data structure.