面向增量插入和查询的度量索引
Toward Metric Indexes for Incremental Insertion and Querying
摘要：
在这项工作中，我们探讨使用度量索引结构，加速最近邻查询，在这种情况下，我们需要在部署过程中交错插入和查询。这个用例的灵感来自于恶意软件分析分类的现实需求，并且令人惊讶地未得到充分研究。现有的文献往往要么只关注最终查询效率，往往不支持增量插入，或不支持任意的距离度量。我们修改和改进了三种算法，以支持我们的情况下，增量插入和查询任意指标，并评估他们对多个数据集和距离指标，而不同的值$k$的所需数量的最近的邻居。在这样做的时候，我们确定我们的改进的最小方差优势点树在这种情况下表现最好。
英文摘要：
In this work we explore the use of metric index structures, which accelerate nearest neighbor queries, in the scenario where we need to interleave insertions and queries during deployment. This use-case is inspired by a real-life need in malware analysis triage, and is surprisingly understudied. Existing literature tends to either focus on only final query efficiency, often does not support incremental insertion, or does not support arbitrary distance metrics. We modify and improve three algorithms to support our scenario of incremental insertion and querying with arbitrary metrics, and evaluate them on multiple datasets and distance metrics while varying the value of $k$ for the desired number of nearest neighbors. In doing so we determine that our improved Vantage-Point tree of Minimum-Variance performs best for this scenario.
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。
