可更新的平衡索引用于大规模新鲜向量的稳定流相似性搜索
Updatable Balanced Index for Stable Streaming Similarity Search over Large-Scale Fresh Vectors
AI总结：
UBIS通过调度并发更新和减少不平衡更新情况，提升了大规模新鲜向量流相似性搜索的准确率和更新吞吐量。
摘要：
随着人工智能日益普及，向量已成为信息检索和推荐等服务中最广泛使用的数据结构之一。近似最近邻搜索（ANNS）通常依赖于优化快速搜索的索引来组织大规模数据集，在这些流行服务中发挥了核心作用。随着数据漂移频率的增长，索引需要能够适应新数据并支持实时更新。现有采用两种不同方法的研究存在以下缺点：1）使用额外缓冲区临时存储新数据的方法由于全局重建过程而资源密集且低效；2）升级内部索引结构的方法由于流工作负载中的更新拥堵和不平衡分布而出现性能下降。在本文中，我们提出了UBIS，一种用于稳定流相似性搜索的可更新平衡索引，通过调度并发更新来解决冲突，并通过减少不平衡更新情况来保持良好的索引质量。在真实世界数据集上的实验结果表明，UBIS在流工作负载中相比最先进的索引在平均搜索准确率上提高了77%，更新吞吐量提高了45%。
英文摘要：
As artificial intelligence gains more and more popularity, vectors are one of the most widely used data structures for services such as information retrieval and recommendation. Approximate Nearest Neighbor Search (ANNS), which generally relies on indices optimized for fast search to organize large datasets, has played a core role in these popular services. As the frequency of data shift grows, it is crucial for indices to accommodate new data and support real-time updates. Existing researches adopting two different approaches hold the following drawbacks: 1) approaches using additional buffers to temporarily store new data are resource-intensive and inefficient due to the global rebuilding processes; 2) approaches upgrading the internal index structure suffer from performance degradation because of update congestion and imbalanced distribution in streaming workloads. In this paper, we propose UBIS, an Updatable Balanced Index for stable streaming similarity Search, to resolve conflicts by scheduling concurrent updates and maintain good index quality by reducing imbalanced update cases, when the update frequency grows. Experimental results in the real-world datasets demonstrate that UBIS achieves up to 77% higher search accuracy and 45% higher update throughput on average compared to the state-of-the-art indices in streaming workloads.
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。
