DESIRE: An efficient dynamic cluster-based forest indexing for similarity search in multi-metric spaces

• Main Authors: ZHU, Yifan, CHEN, Lu, GAO, Yunjun, ZHENG, Baihua, WANG, Pengfei
• 格式: text
• 語言: English
• 出版: Institutional Knowledge at Singapore Management University 2022
• 主題: Databases and Information Systems
• 在線閱讀: https://ink.library.smu.edu.sg/sis_research/7254; https://ink.library.smu.edu.sg/context/sis_research/article/8257/viewcontent/p2121_gao.pdf

Similarity search fnds similar objects for a given query object based on a certain similarity metric. Similarity search in metric spaces has attracted increasing attention, as the metric space can accommodate any type of data and support fexible distance metrics. However, a metric space only models a single data type with a specifc similarity metric. In contrast, a multi-metric space combines multiple metric spaces to simultaneously model a variety of data types and a collection of associated similarity metrics. Thus, a multi-metric space is capable of performing similarity search over any combination of metric spaces. Many studies focus on indexing a single metric space, while only a few aims at indexing multi-metric space to accelerate similarity search. In this paper, we propose DESIRE, an efcient dynamic cluster-based forest index for similarity search in multi-metric spaces. DESIRE frst selects high-quality centers to cluster objects into compact regions, and then employs B+ -trees to efectively index distances between centers and corresponding objects. To support dynamic scenarios, efcient update strategies are developed. Further, we provide fltering techniques to accelerate similarity queries in multi-metric spaces. Extensive experiments on four real datasets demonstrate the superior efciency and scalability of our proposed DESIRE compared with the state-of-the-art multi-metric space indexes.