A Survey of Multi-Dimensional Indexes: Past and Future Trends
Li Ming-xin, Hancheng Wang, Haipeng Dai, Meng Li, Chengliang Chai, Rong Gu, Feng Chen, Zhiyuan Chen, Shuaituan Li, Qizhi Liu, Guihai Chen
Abstract
Index structures are powerful tools for improving query performance and reducing disk access in database systems. Multi-dimensional indexes, in particular, are used to filter records effectively based on multiple attributes. Classical multi-dimensional index structures, such as KD-Tree, Quadtree, and R-Tree, have been widely used in modern databases. However, advancements in hardware and algorithms have led to the emergence of new types of multi-dimensional index structures. In this paper, we begin by reviewing classical multi-dimensional indexes. Next, we explore the approaches that leverage modern hardware features, such as Solid-State Drive, Non-Volatile Memory, Dynamic Random Access Memory, and Graphics Processing Unit, to improve the performance of multi-dimensional indexes in various aspects. Then, we investigate the novel work of multi-dimensional indexes that apply state-of-the-art machine learning techniques. Finally, we discuss the challenges and future research directions for multi-dimensional indexing methods.