Enterprise Connection Graph Analysis with ML Pattern Recognition
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Solution Overview
Problem
Current technologies lack mechanisms for accurately and efficiently analyzing connections and relationships between enterprises, leading to inefficient communications and wasteful use of resources in establishing connections between organizations.
Innovation Solution
A data processing system that aggregates connection data from multiple sources, generates a connection graph using a graph generating engine, and employs trained machine-learning models to identify patterns and provide recommendations for improving enterprise-to-enterprise connections, considering the context of each enterprise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If connection data is aggregated from multiple data sources to improve analysis accuracy, then the completeness of connection information is improved, but the complexity of data processing increases
Solution Approach 1:
The system segments the complex data processing task into distinct functional modules: a connection graph generating engine that processes raw connection data from multiple sources, and trained machine-learning models that analyze patterns in the generated graphs. This segmentation allows each component to specialize in specific aspects of data processing, improving overall accuracy while managing complexity through modular architecture.
Solution Approach 2:
The connection graph serves as an intermediary data structure that transforms raw connection data from multiple sources into a standardized format suitable for ML model analysis. This intermediary representation simplifies the processing burden by pre-organizing data in a meaningful structure before it reaches the analytical models.
2Productivity
If machine-learning models are used to identify connection patterns, then the efficiency of pattern recognition is improved, but the computational resources required increase
Solution Approach 1:
Connection graphs are generated in advance from raw data before being fed into ML models for pattern recognition. This preliminary processing step pre-organizes the data structure and extracts basic features, reducing the computational burden on subsequent ML models and enabling more efficient pattern recognition with lower resource requirements.
Solution Approach 2:
The system creates simplified representations (connection graphs) that copy only the essential structural information from raw connection data, excluding redundant details. This copied representation maintains the necessary pattern information for ML analysis while significantly reducing data volume and associated computational resource requirements.
3Measurement precision
If connection graphs are generated from aggregated data, then the accuracy of connection representation is improved, but the time required for data processing increases
Solution Approach 1:
The connection graph generating engine operates continuously to maintain up-to-date connection representations as new data arrives from multiple sources. This continuous processing approach ensures accurate connection representation is maintained without requiring periodic batch processing, thereby reducing overall processing time while preserving accuracy.
Data Source
AI summary
A system and method for analyzing connections between a first enterprise and a second enterprise includes retrieving connection data related to connections between the first enterprise and the second enterprise from a plurality of data sources and utilizing a connection graph generating engine to generate a connection graph for the connections between the first enterprise and the second enterprise, where the connection graph is generated based on the retrieved connection data. The connection graph is provided as an input to a trained machine-learning (ML) model to identify connection patterns in the connections between the first enterprise and the second enterprise and recommendations are generated using a second trained ML model based on the identified connection patterns and based on at least one of a context of the first enterprise or the second enterprise. The recommendations are provided for display to a user.