Knowledge Graph Dashboard for Industrial Asset Data Correlation
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 and resource-intensive processes.
Innovation Solution
A system utilizing a knowledge graph to correlate operational technology data and provide insights, allowing for automated dashboard visualizations and adjustments to operational settings, facilitated by an AI-driven cognitive advisor for enterprise data management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data analytics methods are used with human interaction, then data can be analyzed, but the process is time-consuming and resource-intensive
Solution Approach 1:
The system enables automated data analysis through AI-driven cognitive advisors that independently process and analyze data from multiple systems without requiring human intervention. The knowledge graph automatically correlates data points and generates insights, allowing the system to serve itself in the data analysis process.
Solution Approach 2:
Manual human interaction in data analysis is replaced by automated computational systems. The patent substitutes mechanical human processes with electronic data processing, machine learning algorithms, and automated correlation engines that can analyze data much faster and with fewer resources.
2Loss of information
If data from multiple systems is correlated manually, then inter-relationships can be determined, but the process is difficult and time-consuming
Solution Approach 1:
The knowledge graph serves as a universal data structure that can correlate data from multiple different systems and data types simultaneously. It provides a multi-functional platform that handles various data formats and relationship types through a single unified approach, making the correlation process efficient and comprehensive.
Solution Approach 2:
The knowledge graph acts as an intermediary layer between multiple data systems. It mediates the correlation process by standardizing data representations and providing a common framework for establishing relationships across diverse data sources, thereby simplifying the complex task of determining inter-relationships.
3Loss of energy
If limited time is spent on data modeling, then resources are conserved, but insights from asset data are reduced
Solution Approach 1:
The system performs preliminary data modeling and correlation in advance by continuously building and updating the knowledge graph with data from multiple systems. This preliminary action prepares the data structure so that when analysis is needed, the foundational relationships are already established, reducing the need for intensive real-time computational resources.
Solution Approach 2:
The knowledge graph maintenance and data correlation processes run continuously in the background, constantly refining and updating relationships between data points. This continuous useful action ensures that insights are always available without requiring periodic intensive computational bursts, thereby conserving resources while maintaining high-quality insights.
Data Source
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.