iSPEED: a Scalable and Distributed In-Memory Based Spatial Query System for Large and Structurally Complex 3D Data
Summary: iSPEED is a scalable, distributed in-memory spatial query system for massive, complex 3D pathology data. It uses progressive compression with multi-level detail, in-memory/global indexes plus on-demand indexing, and structural indexing to speed 3D joins, nearest neighbor, and proximity queries via REST. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Hoang Vo (State University of New York at Stony Brook)
- 2. Yanhui Liang (Google; State University of New York at Stony Brook)
- 3. Jun Kong (Emory University)
- 4. Fusheng Wang (State University of New York at Stony Brook)
BibTeX Citation
@article{vo_vldb18,
title = {{iSPEED: a Scalable and Distributed In-Memory Based Spatial Query System for Large and Structurally Complex 3D Data}},
author = {Vo, Hoang and Liang, Yanhui and Kong, Jun and Wang, Fusheng},
journal = {PVLDB},
series = {{VLDB} '18},
volume = {11},
number = {12},
pages = {2078--2081},
doi = {10.14778/3229863.3236264},
url = {https://doi.org/10.14778/3229863.3236264},
year = {2018}
}
Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,332 | High-Performance Spatial Data Analytics: Systematic R&D for Scale-Out and Scale-Up Solutions from the Past to Now | 2024 | VLDB | 5.093636e-05 |
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