Raptor: Large Scale Analysis of Big Raster and Vector Data
Summary: Raptor enables large-scale zonal statistics without raster–vector conversion by directly combining raster and vector data. It benchmarks three approaches—vector-based, raster-based, and Raptor—showing how cross-representation efficiency varies with dataset size. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Samriddhi Singla
- 2. Ahmed Eldawy
- 3. Rami Alghamdi
- 4. Mohamed F. Mokbel
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,917 | Array DBMS: Past, Present, and (Near) Future | 2021 | VLDB | 4.6173899e-05 |
| 10,864 | RDPro: Distributed Processing of Big Raster Data | 2025 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 860 | The Multidimensional Database System RasDaMan | 1998 | SIGMOD | 0.00015860465 |
| 1,435 | Simba: Efficient In-Memory Spatial Analytics | 2016 | SIGMOD | 0.00012004456 |
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