Dumpy：一种面向大规模数据序列集合的紧凑自适应索引
Dumpy: A Compact and Adaptive Index for Large Data Series Collections
AI总结：
针对iSAX索引搜索准确率低和DSTree构建时间长的问题，本文提出Dumpy索引及其变体Dumpy-Fuzzy，通过多叉自适应结构和序列复制显著提升大规模数据序列索引的效率、可扩展性与搜索准确性。
摘要：
数据序列索引对于管理和分析当今日益增长的大规模数据序列集合是必不可少的。这些索引同时支持精确和近似相似性搜索，其中近似搜索能够在毫秒级内提供高质量结果，这使其对某些现代应用极具吸引力。降低预处理（即索引构建）时间并提高搜索结果的准确性是两个主要挑战。DSTree和iSAX索引系列是该问题的最先进解决方案。然而，DSTree存在索引构建时间长的问题，而iSAX则存在搜索准确率低的问题。在本文中，我们识别出iSAX索引系列中两个对整体性能产生不利影响的问题。首先，我们观察到与索引结构设计（即节点扇出度）相关的邻近性-紧凑性权衡的存在，这显著限制了所得索引的效率和准确性。其次，偏斜的数据分布会对iSAX的性能产生负面影响。为了克服这些问题，我们提出了Dumpy，这是一种采用新颖多叉数据结构、自适应节点分裂算法和高效构建工作流的索引。此外，我们设计了Dumpy-Fuzzy作为Dumpy的一种变体，它通过对序列进行适当的复制来进一步提高搜索准确性。在多种大规模真实数据集上的实验表明，Dumpy解决方案在效率、可扩展性和搜索准确性方面均显著优于其竞争对手。本文发表于SIGMOD'23。
英文摘要：
Data series indexes are necessary for managing and analyzing the increasing amounts of data series collections that are nowadays available. These indexes support both exact and approximate similarity search, with approximate search providing high-quality results within milliseconds, which makes it very attractive for certain modern applications. Reducing the pre-processing (i.e., index building) time and improving the accuracy of search results are two major challenges. DSTree and the iSAX index family are state-of-the-art solutions for this problem. However, DSTree suffers from long index building times, while iSAX suffers from low search accuracy. In this paper, we identify two problems of the iSAX index family that adversely affect the overall performance. First, we observe the presence of a proximity-compactness trade-off related to the index structure design (i.e., the node fanout degree), significantly limiting the efficiency and accuracy of the resulting index. Second, a skewed data distribution will negatively affect the performance of iSAX. To overcome these problems, we propose Dumpy, an index that employs a novel multi-ary data structure with an adaptive node splitting algorithm and an efficient building workflow. Furthermore, we devise Dumpy-Fuzzy as a variant of Dumpy which further improves search accuracy by proper duplication of series. Experiments with a variety of large, real datasets demonstrate that the Dumpy solutions achieve considerably better efficiency, scalability and search accuracy than its competitors. This paper was published in SIGMOD'23.
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。
