Xling：一种用于加速高维近似相似连接的学习型过滤器框架
Xling: A Learned Filter Framework for Accelerating High-Dimensional Approximate Similarity Join
AI总结：
本文提出学习型过滤器框架Xling及基于其的相似连接方法XJoin，通过预测并跳过邻居不足的查询来加速高维近似相似连接，并可迁移到新数据集或作为插件嵌入其他方法。
摘要：
相似连接找出给定距离阈值内的所有近邻点对。已有许多相似连接方法被提出，但由于维度灾难和数据无感知性，它们在高维空间上通常效率不高。我们研究了使用度量空间布隆过滤器（metric space Bloom filter, MSBF）——一类用于检查查询点在多维空间中是否存在邻居的数据结构——来加速相似连接的可能性。然而，将MSBF应用于相似连接面临若干挑战，包括信息损失过大、数据无感知性以及对距离度量的硬性约束。在本文中，我们提出Xling，一个通用框架，用于基于任意现有回归模型构建学习型度量空间过滤器，旨在准确预测查询点是否具有足够数量的邻居。该框架提供了一组优化策略，以基于学习模型进一步提升预测质量，实验证明其预测质量显著高于现有MSBF。我们还提出了XJoin，这是基于Xling的首批基于过滤器的相似连接方法之一。通过预测并跳过那些没有足够邻居的查询，XJoin能够有效减少不必要的邻居搜索，从而实现显著的加速。得益于深度学习模型的泛化能力，XJoin可以轻松迁移到新数据集（在相似分布下）而无需重新训练。此外，Xling不仅限于应用于XJoin，它还可以作为一个灵活的插件，插入任何基于循环的相似连接方法中以实现加速。
英文摘要：
Similarity join finds all pairs of close points within a given distance threshold. Many similarity join methods have been proposed, but they are usually not efficient on high-dimensional space due to the curse of dimensionality and data-unawareness. We investigate the possibility of using metric space Bloom filter (MSBF), a family of data structures checking if a query point has neighbors in a multi-dimensional space, to speed up similarity join. However, there are several challenges when applying MSBF to similarity join, including excessive information loss, data-unawareness and hard constraint on the distance metric. In this paper, we propose Xling, a generic framework to build a learning-based metric space filter with any existing regression model, aiming at accurately predicting whether a query point has enough number of neighbors. The framework provides a suite of optimization strategies to further improve the prediction quality based on the learning model, which has demonstrated significantly higher prediction quality than existing MSBF. We also propose XJoin, one of the first filter-based similarity join methods, based on Xling. By predicting and skipping those queries without enough neighbors, XJoin can effectively reduce unnecessary neighbor searching and therefore it achieves a remarkable acceleration. Benefiting from the generalization capability of deep learning models, XJoin can be easily transferred onto new dataset (in similar distribution) without re-training. Furthermore, Xling is not limited to being applied in XJoin, instead, it acts as a flexible plugin that can be inserted to any loop-based similarity join methods for a speedup.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
