arXiv:2606.24407v1 Announce Type: cross Abstract: We consider an oracle that processes a limited batch of records at a time and clusters those that refer to the same real-world entity. We study how to interrogate such an oracle to resolve entities in a dataset whose size is far larger than a single batch, and where no batch is guaranteed to contain all records of any given entity. We aim at a pay-as-you-go approach, to have full control over the costs (the number of oracle consults), while achieving the highest possible recall at every step. We formally cast this problem as batched entity reso
This research addresses a fundamental challenge in data integration and AI scalability, particularly relevant as datasets grow exponentially and efficient entity resolution becomes critical for AI system performance.
Improving entity resolution via limited oracle queries directly impacts the efficiency and cost-effectiveness of managing large, diverse datasets, which is crucial for the development and deployment of sophisticated AI agents and data analysis systems.
The proposed 'pay-as-you-go' method offers a more controlled and cost-efficient approach to entity resolution, moving away from brute-force methods towards optimized, iterative querying strategies.
- · AI/ML data architects
- · Data integration platforms
- · Companies with large, messy datasets
- · AI agent developers
- · Inefficient brute-force data cleaning systems
- · Manual data reconciliation services
More accurate and cost-effective data cleaning for AI models.
Accelerated development and deployment of autonomous AI agents benefiting from higher quality input data.
Enhanced trust and reliability in AI-driven decision-making across various sectors due to improved foundational data consistency.
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Read at arXiv cs.AI