Vo Hoang, Liang Yanhui, Kong Jun, Wang Fusheng
Stony Brook University, Stony Brook, NY, USA.
Emory University, Atlanta, GA, USA.
Proceedings VLDB Endowment. 2018 Aug;11(12):2078-2081. doi: 10.14778/3229863.3236264.
The recent technological advancement in digital pathology has enabled 3D tissue-based investigation of human diseases at extremely high resolutions. Discovering and verifying spatial patterns among massive 3D micro-anatomic biological objects such as blood vessels and cells derived from 3D pathology image volumes plays a pivotal role in understanding diseases. However, the exponential increase of available 3D data and the complex structures of biological objects make it extremely difficult to support spatial queries due to high I/O, communication and computational cost for 3D spatial queries. In this demonstration, we present our scalable in-memory based spatial query system for large-scale 3D data with complex structures. Low latency is managed by storing in memory with progressive compression including successive levels of detail on object level. On the other hand, low computational cost is achieved by pre-generation of global spatial indexes in memory and additional on-demand generation of indexing at run-time. Furthermore, iSPEED applies structural indexing on complex structured objects in multiple query types to gain performance advantage. During query processing, the memory footprint of iSPEED is minimal due to its indexing structure and progressive decompression on-demand. We demonstrate iSPEED query capability with three representative queries: 3D spatial joins, nearest neighbor and spatial proximity estimation on multiple datasets using a web based RESTful interface. Users can furthermore explore the input data structure, manage and adjust query pipeline parameters on the interface.
Hoang Vo, Yanhui Liang, Jun Kong, and Fusheng Wang. iSPEED: a Scalable and Distributed In-Memory Based Spatial Query System for Large and Structurally Complex 3D Data.
数字病理学领域最近的技术进步使得能够以极高分辨率对人类疾病进行基于3D组织的研究。在源自3D病理学图像体的大量3D微观解剖生物对象（如血管和细胞）中发现并验证空间模式对于理解疾病起着关键作用。然而，可用3D数据的指数级增长以及生物对象的复杂结构使得支持空间查询极其困难，因为3D空间查询的I/O、通信和计算成本很高。在本演示中，我们展示了针对具有复杂结构的大规模3D数据的基于内存的可扩展空间查询系统。通过在内存中存储并采用渐进式压缩（包括在对象级别上的连续细节层次）来管理低延迟。另一方面，通过在内存中预先生成全局空间索引以及在运行时额外按需生成索引来实现低计算成本。此外，iSPEED在多种查询类型中对复杂结构对象应用结构索引以获得性能优势。在查询处理过程中，由于其索引结构和按需渐进式解压缩，iSPEED的内存占用最小。我们使用基于Web的RESTful接口通过三个代表性查询展示了iSPEED的查询能力：对多个数据集进行3D空间连接、最近邻查询和空间邻近估计。用户还可以在该接口上探索输入数据结构、管理并调整查询管道参数。
PVLDB参考文献格式：黄武、梁艳慧、孔军、王福生。iSPEED：一种用于大型且结构复杂的3D数据的基于内存的可扩展分布式空间查询系统。
