Entity Discovery Model Relay Identification
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Solution Overview
Problem
Existing systems face challenges in establishing communication links with entities that are not onboarded or have unavailable connections, hindering the delivery of electronic alert messages, particularly in scenarios where direct communication is absent.
Innovation Solution
The entity discovery model identifies a relay entity associated with the target entity by executing multiple discovery stages, including inference mapping, temporal logic, data warehouse mining, and machine-learning models, which can operate in either waterfall or parallel execution modes to mitigate computational overhead and ensure effective entity identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple discovery stages are executed to identify relay entities, then entity identification accuracy is improved, but computational overhead increases
Solution Approach 1:
The entity discovery process is divided into multiple independent discovery stages, each focusing on specific entity types or relationship patterns. This segmentation allows the system to execute only relevant stages based on the target entity characteristics, improving identification accuracy while controlling computational overhead by avoiding unnecessary stage executions.
Solution Approach 2:
The system performs preliminary filtering and prioritization of discovery stages before full execution. By pre-assessing which stages are most likely to yield results based on target entity attributes, the system prepares an optimized execution plan that maintains high identification accuracy while minimizing computational resources consumed.
2Reliability
If multiple discovery stages are executed to identify relay entities, then message delivery reliability is improved, but processing time increases
Solution Approach 1:
The discovery stage execution is made dynamic and adaptive. The system adjusts the number and type of stages executed based on real-time factors such as target entity characteristics, available communication channels, and urgency of message delivery. This dynamic approach ensures high delivery reliability when needed while reducing processing time when rapid delivery is critical.
Solution Approach 2:
The system implements periodic discovery stage execution with varying depths. For time-critical messages, only essential stages are executed in a condensed manner. For non-urgent messages, the full multi-stage discovery process runs periodically to ensure high delivery reliability, thus balancing processing time and reliability requirements.
3Reliability
If relay entities are identified through multiple discovery stages, then communication link availability is improved, but system complexity increases
Solution Approach 1:
The patent introduces intermediary components such as entity relationship graphs and communication channel registries that mediate between the discovery stages and the message delivery system. These intermediaries standardize the complexity management by providing unified interfaces and data structures, allowing multiple discovery stages to execute while maintaining manageable system complexity through abstraction layers.
4Productivity
If discovery stages are executed in parallel, then processing efficiency is improved, but coordination complexity increases
Solution Approach 1:
The parallel discovery stage execution incorporates feedback mechanisms where each stage reports its progress, findings, and status to a central coordinator. This feedback loop enables dynamic adjustment of parallel execution, allowing the system to optimize processing efficiency by continuing successful stages while terminating or modifying stages that consume excessive resources or yield no results, thus managing coordination complexity through active monitoring and adaptation.
Data Source
AI summary
The disclosure relates to methods and systems of performing entity discovery based on an entity discovery model. Entity discovery refers to one or more computational processes that attempt to identify an entity when the identity of the entity is unknown. The entity discovery model may include multiple discovery stages. Each discovery stage of the entity discovery model may independently attempt to identify an entity. The entity discovery model may execute the discovery stages in a waterfall execution in which a discovery stage is executed only if a prior discovery stage failed to identify the entity, potentially reducing the computational overhead of executing all discovery stages. In other examples, the entity discovery model may execute the discovery stages in a parallel execution to minimize the time it takes to perform entity discovery.