Clustered Entity Matching Across Multiple Optimization Queues
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional task scheduling techniques often result in suboptimal solutions due to locally optimal choices and fail to achieve overall optimal goals in systems managing multiple task queues, particularly when optimizing domain-specific criteria.
Innovation Solution
An online system uses machine learning models to match source and target entities by generating feature vectors, clustering entities based on vector distances, and optimizing an aggregate score across multiple queues, allowing for dynamic adjustments to improve overall matching efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional scheduling techniques (first come first served or round robin) are used, then the system is simple to implement, but the matching quality based on domain-specific criteria deteriorates
Solution Approach 1:
The system segments the matching process into multiple stages: initial clustering based on domain-specific criteria, followed by queue-based scheduling. This allows the system to maintain simplicity in basic operations while achieving high matching quality through structured segmentation of the matching task.
Solution Approach 2:
The system dynamically adjusts the scheduling strategy by maintaining multiple queues for different domain-specific criteria and allowing flexible assignment of entities to queues based on real-time matching requirements. This dynamic approach enables the system to adapt to varying optimization needs while maintaining operational simplicity.
2Productivity
If greedy scheduling strategies are used, then local optimal choices are made at each step, but overall optimal solutions are not achieved
Solution Approach 1:
The system divides the overall matching problem into multiple independent queues, each optimized for specific domain criteria. This segmentation allows greedy strategies to work effectively within each queue for local optimization, while the global optimizer coordinates across queues to achieve overall optimal solutions.
Solution Approach 2:
The system adds a global optimization dimension by introducing a coordinator that views the matching problem holistically across all queues. This dimensional shift from local queue-level decisions to global system-level optimization enables the system to achieve overall optimal solutions while maintaining efficient local processing.
3Measurement precision
If multiple queues are maintained for different domain criteria, then matching quality improves, but system complexity increases
Solution Approach 1:
The queue manager is designed as a universal component that handles multiple queues with different domain criteria using the same underlying data structures and algorithms. This multi-functionality reduces system complexity by reusing proven components across different matching scenarios.
Solution Approach 2:
The system introduces a coordinator as an intermediary between the multiple queues and the matching algorithms. This coordinator simplifies the system architecture by providing a unified interface for queue management and coordination, abstracting the complexity of multiple queues from the core matching logic.
Data Source
AI summary
An online system matches source and target entities using multiple queues. The online system performs clustering of target entities to generate a plurality of clusters based on vector distances between pairs of target entities. Each cluster comprises target entities that are close to other target entities within the cluster compared to target entities of remaining clusters. The online system optimizes an aggregate score across the plurality of entities using a plurality of queues. Each queue is associated with a source entity and includes one or more target entities. The online system identifies a cluster of target entities based on a distance between the source entities and entities of the cluster of target entities. The online system selects a subset of target entities from the identified cluster using a machine learning based model trained to determine a score between an input source entity and target entity.