iSPEED: a Scalable and Distributed In-Memory Based Spatial Query System for Large and Structurally Complex 3D Data

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 iSPEED 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. PVLDB REFERENCE FORMAT: 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.