Graph Data Enrichment With Software-Agent Feedback Loops
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Solution Overview
Problem
Current data management systems in automation environments, particularly those using graph databases, are static and do not benefit from independent downstream processing, leading to inefficiencies in data storage and retrieval, as they do not automatically reflect the current state of data sources or provide real-time updates.
Innovation Solution
The implementation of a platform that automatically discovers, extracts, maps, merges, and enriches data from various sources within automated industrial and commercial environments, using software agents to identify patterns and update a graph database, providing normalized, merged, and enriched data through APIs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a graph database is used to store data structure and relationships, then data organization and query capability are improved, but the system becomes static and cannot automatically reflect real-time changes from data sources
Solution Approach 1:
The system implements feedback mechanisms where software agents continuously monitor data sources and automatically update the graph database when changes are detected. This creates a closed-loop system where the database reflects real-time state changes from IoT devices and data sources, resolving the contradiction between structured storage and dynamic adaptability
Solution Approach 2:
The patent transforms the static graph database into a dynamic system by introducing software agents that continuously discover, extract, and incorporate new data. The database structure evolves automatically as agents identify patterns and relationships, enabling real-time adaptability while maintaining organized data architecture
2Adaptability or versatility
If downstream applications perform independent processing, then application flexibility is improved, but the graph database does not benefit from such processing and remains outdated
Solution Approach 1:
The system establishes feedback loops where downstream application processing results are captured and fed back into the graph database. Software agents monitor application queries and processing outcomes, then use this information to enrich and update the database, ensuring it benefits from downstream insights while applications maintain independence
Solution Approach 2:
The graph database serves multiple functions simultaneously: it acts as a structured data repository, a real-time reflection of data source states, and a beneficiary of downstream application processing. This multi-functionality allows the database to both provide independent query capability and continuously learn from application interactions
3Measurement precision
If manual updates are performed on the graph database, then data accuracy is improved, but the speed and responsiveness of data enrichment are reduced
Solution Approach 1:
The system implements self-service automation where software agents autonomously discover data sources, extract relevant information, validate data quality, and update the graph database without manual intervention. This automated self-service process maintains high data accuracy through validation rules while achieving rapid continuous enrichment
Solution Approach 2:
Software agents perform preliminary actions by continuously monitoring data sources and pre-processing data before it needs to be reflected in the database. This proactive approach ensures data is ready for immediate incorporation, maintaining both accuracy through pre-validation and speed through advance preparation
Data Source
AI summary
Described are platforms, systems, and methods for real-time enrichment of vertices, edges, and related data within a graph database. The platforms, systems, and methods maintain a graph database comprising a representation of a current state of an automation environment comprising a plurality of data sources, wherein the data sources are represented as vertices in the graph database and relationships between the individual data sources are represented as edges in the graph database; operate a plurality of software agents, each software agent configured to perform operations comprising: applying an algorithm to identify patterns in the graph database; and generating a specific data enrichment based on one or more identified patterns; and contribute the generated data enrichment back to the graph database.