基于非线性k距离近似的存储高效RkNN检索
Memory-Efficient RkNN Retrieval by Nonlinear k-Distance Approximation
摘要：
反向k-最近邻（RkNN）查询是一种已建立的查询类型，具有各种应用，从识别高度影响力的对象到增量更新kNN图，以优化传感器通信和离群值检测。最先进的解决方案利用现实世界数据集中的k距离通常遵循幂律分布，并将它们与对数-对数空间中的线性线绑定。在这项工作中，我们调查了这一假设，并发现它在密度变化的区域中被违反，我们表明这是现实生活中数据集的典型特征。对于一个通用的解决方案，我们提出的k-距离的估计作为一个回归问题。因此，我们能够利用丰富的可用机器学习模型的力量，并从它们的进步中获利。我们提出了一种灵活的方法，可以引导性能-内存消耗的权衡，特别是在边缘计算的背景下找到具有固定内存预算的良好解决方案。此外，我们展示了如何获得和提高保证边界必不可少的精确查询处理。在真实数据集上的实验中，我们展示了这个框架如何显着减少索引内存消耗，并大大减少候选集的大小。我们在https://github.com/sobermeier/nonlinear-kdist上发布代码。
英文摘要：
The reverse k-nearest neighbor (RkNN) query is an established query type with various applications reaching from identifying highly influential objects over incrementally updating kNN graphs to optimizing sensor communication and outlier detection. State-of-the-art solutions exploit that the k-distances in real-world datasets often follow the power-law distribution, and bound them with linear lines in log-log space. In this work, we investigate this assumption and uncover that it is violated in regions of changing density, which we show are typical for real-life datasets. Towards a generic solution, we pose the estimation of k-distances as a regression problem. Thereby, we enable harnessing the power of the abundance of available Machine Learning models and profiting from their advancement. We propose a flexible approach which allows steering the performance-memory consumption trade-off, and in particular to find good solutions with a fixed memory budget crucial in the context of edge computing. Moreover, we show how to obtain and improve guaranteed bounds essential to exact query processing. In experiments on real-world datasets, we demonstrate how this framework can significantly reduce the index memory consumption, and strongly reduce the candidate set size. We publish our code at https://github.com/sobermeier/nonlinear-kdist.
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。
