DumpyOS：面向可扩展数据序列相似性搜索的数据自适应多叉索引
DumpyOS: A Data-Adaptive Multi-ary Index for Scalable Data Series Similarity Search
AI总结：
针对现有数据序列索引构建慢、准确率低的问题，提出自适应多叉索引Dumpy及其变体，并行化得到DumpyOS并优化近似搜索算法，兼顾构建效率与搜索精度。
摘要：
数据序列索引对于管理和分析当今日益增多的可用数据序列集合必不可少。这类索引同时支持精确相似性搜索和近似相似性搜索，其中近似搜索可在毫秒级时间内返回高质量结果，因此对部分现代应用极具吸引力。缩短预处理（即索引构建）时间并提升搜索结果准确率是两大核心挑战。DSTree和iSAX索引家族是该问题的当前最优解决方案，但DSTree存在索引构建耗时长的问题，而iSAX则存在搜索准确率低的缺陷。\n本文指出了iSAX索引家族存在的两个会对整体性能产生负面影响的问题：第一，我们发现索引结构设计（即节点扇出度）存在邻近性-紧凑性权衡问题，这会显著限制最终索引的效率与准确率；第二，倾斜的数据分布会对iSAX的性能造成不利影响。\n为解决上述问题，我们提出了Dumpy索引，它采用新型多叉数据结构，配备自适应节点分裂算法和高效的构建工作流。此外，我们设计了Dumpy的变体Dumpy-Fuzzy，通过对序列进行合理复制进一步提升搜索准确率。为充分利用多核CPU、固态硬盘（SSD）等现代硬件的潜力，我们借助完善的索引机制和基于剪枝的查询算法，将Dumpy并行化为DumpyOS。我们还提出了优化的近似搜索算法DumpyOS-F，该算法可在不破坏索引的前提下显著提升搜索准确率。
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. To fully leverage the potential of modern hardware including multicore CPUs and Solid State Drives (SSDs), we parallelize Dumpy to DumpyOS with sophisticated indexing and pruning-based querying algorithms. An optimized approximate search algorithm, DumpyOS-F which prominently improves the search accuracy without violating the index, is also proposed.
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