SPER: Accelerating Progressive Entity Resolution via Stochastic Bipartite Maximization
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909979648196608 |
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| author | Karapiperis, Dimitrios Papadakis, George Verykios, Vassilios |
| author_facet | Karapiperis, Dimitrios Papadakis, George Verykios, Vassilios |
| contents | Entity Resolution (ER) is a critical data cleaning task for identifying records that refer to the same real-world entity. In the era of Big Data, traditional batch ER is often infeasible due to volume and velocity constraints, necessitating Progressive ER methods that maximize recall within a limited computational budget. However, existing progressive approaches fail to scale to high-velocity streams because they rely on deterministic sorting to prioritize candidate pairs, a process that incurs prohibitive super-linear complexity and heavy initialization costs. To address this scalability wall, we introduce SPER (Stochastic Progressive ER), a novel framework that redefines prioritization as a sampling problem rather than a ranking problem. By replacing global sorting with a continuous stochastic bipartite maximization strategy, SPER acts as a probabilistic high-pass filter that selects high-utility pairs in strictly linear time. Extensive experiments on eight real-world datasets demonstrate that SPER achieves significant speedups (3x to >6x) over state-of-the-art baselines while maintaining comparable recall and precision. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_23491 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SPER: Accelerating Progressive Entity Resolution via Stochastic Bipartite Maximization Karapiperis, Dimitrios Papadakis, George Verykios, Vassilios Databases Entity Resolution (ER) is a critical data cleaning task for identifying records that refer to the same real-world entity. In the era of Big Data, traditional batch ER is often infeasible due to volume and velocity constraints, necessitating Progressive ER methods that maximize recall within a limited computational budget. However, existing progressive approaches fail to scale to high-velocity streams because they rely on deterministic sorting to prioritize candidate pairs, a process that incurs prohibitive super-linear complexity and heavy initialization costs. To address this scalability wall, we introduce SPER (Stochastic Progressive ER), a novel framework that redefines prioritization as a sampling problem rather than a ranking problem. By replacing global sorting with a continuous stochastic bipartite maximization strategy, SPER acts as a probabilistic high-pass filter that selects high-utility pairs in strictly linear time. Extensive experiments on eight real-world datasets demonstrate that SPER achieves significant speedups (3x to >6x) over state-of-the-art baselines while maintaining comparable recall and precision. |
| title | SPER: Accelerating Progressive Entity Resolution via Stochastic Bipartite Maximization |
| topic | Databases |
| url | https://arxiv.org/abs/2512.23491 |