Proactive Data Routing with Machine-Learning Path Adaptation
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Solution Overview
Problem
Existing data management systems struggle with efficiently verifying the state and sequence of complex activities and processes, particularly in network environments, requiring constant adjustments and modifications to handle diverse workloads.
Innovation Solution
The implementation of an adaptive data management system that employs proactive and automated data retention, analysis, and presentation of relevant information to users, utilizing machine-learning models and contextual data graphs to optimize data routing and management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data management systems are used to verify the state and sequence of complex activities, then data gathering can be performed, but the process becomes intensive and inefficient
Solution Approach 1:
The system proactively collects and stores data in advance before verification is needed. Data collectors continuously gather data from various sources and store it in data structures, so when verification is required, the data is already available immediately without intensive gathering operations at that moment.
Solution Approach 2:
The patent introduces intermediate data structures and data collectors as mediators between data sources and verification processes. These intermediaries pre-process and organize data, transforming raw data into verified state information that can be quickly retrieved and used for verification without direct intensive querying of source systems.
2Speed
If data management systems attempt to verify complex processes in real-time, then timely insights can be provided, but system complexity increases
Solution Approach 1:
The system divides the data verification process into separate modular components: data collectors that gather data from specific sources, data structures that organize different types of data, and verification mechanisms that query these structures. This segmentation allows each component to be independently optimized and managed, reducing overall system complexity while enabling fast verification.
Solution Approach 2:
Intermediate data structures serve as mediators between complex data sources and simple verification queries. These structures pre-organize data in ways that make verification straightforward, acting as a buffer that simplifies the interface between data collection complexity and verification simplicity.
3Adaptability or versatility
If traditional data routing is used, then data can be transmitted, but constant adjustments and modifications are required to handle diverse workloads
Solution Approach 1:
The system implements dynamic data routing where data collectors and data structures can be configured and adjusted based on workload characteristics. The architecture allows runtime modifications to data collection priorities, storage structures, and routing rules without requiring system-wide reconfiguration, enabling flexible adaptation to diverse workloads.
Solution Approach 2:
The patent creates a universal data management architecture where the same core components (data collectors, data structures, verification mechanisms) can handle multiple types of workloads and data sources. This multi-functionality reduces the need for constant adjustments because the system can accommodate diverse requirements through configuration rather than structural modification.
Data Source
AI summary
Methods, apparatus, systems, and articles of manufacture are disclosed for proactive data routing. An example apparatus includes at least one memory, machine-readable instructions, and processor circuitry to execute the machine-readable instructions to at least execute a machine-learning model to output a first data routing path in a network environment based on metadata associated with an event in the network environment. The processor circuitry is further to, after a detection of a change of the first data routing path to a second data routing path, retrain the machine-learning model to output a third data routing path based on the second data routing path. The processor circuitry is additionally to cause transmission of a second message to a first node based on the third data routing path after an identification of the event.