Indexing data on flash-based Solid State Drives (SSDs) is an important paradigm recently applied in spatial data management. During last years, the design of new spatial access methods for SSDs, named flash-aware spatial indices, has attracted the attention of many researchers, mainly to exploit the advantages of SSDs in spatial query processing. eFIND is a generic framework for transforming a disk-based spatial index into a flash-aware one, taking into account the intrinsic characteristics of SSDs. In this article, we present a systematic approach for porting disk-based data-driven and space-driven access methods to SSDs, through the eFIND framework. We also present the actual porting of representatives data-driven (R-trees, R*-trees, and Hilbert R-trees) and space-driven (xBR⁺-trees) access methods through this framework. Moreover, we present an extensive experimental evaluation that compares the performance of these ported indices when inserting and querying synthetic and real point datasets. The main conclusions of this experimental study are that the eFIND R-tree excels in insertions, the eFIND xBR⁺-tree is the fastest for different types of spatial queries, and the eFIND Hilbert R-tree is efficient for processing intersection range queries.

基于闪存的固态驱动器 (SSD) 上的数据索引是最近应用于空间数据管理的重要范例。近年来，SSD空间访问新方法的设计，即闪存感知空间索引，引起了许多研究人员的关注，主要是利用SSD在空间查询处理方面的优势。eFIND 是一个通用框架，用于将基于磁盘的空间索引转换为闪存感知索引，同时考虑到 SSD 的内在特性。在本文中，我们提出了一个系统的方法用于通过 eFIND 框架将基于磁盘的数据驱动和空间驱动的访问方法移植到 SSD。我们还通过该框架展示了代表数据驱动（R-trees、R*-trees 和 Hilbert R-trees）和空间驱动（xBR ⁺ -trees）访问方法的实际移植。此外，我们提出了一个广泛的实验评估，比较了这些移植索引在插入和查询合成点数据集和真实点数据集时的性能。本实验研究的主要结论是eFIND R-tree在插入方面表现出色，eFIND xBR ⁺ -tree对于不同类型的空间查询速度最快，eFIND Hilbert R-tree对于处理相交范围查询效率高。