VIP-Tree: An Effective Index for Indoor Spatial Queries
Summary: VIP-Tree proposes IPTree and VIP-Tree, lightweight indexes that exploit indoor venue properties to outperform outdoor indoor methods. Supports near-optimal shortest-distance/path queries and efficient kNN/range queries, with experiments showing orders-of-magnitude gains. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zhou Shao
- 2. Muhammad Aamir Cheema
- 3. David Taniar
- 4. Hua Lu
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,078 | Continuously Monitoring Alternative Shortest Paths on Road Networks | 2020 | VLDB | 4.400728e-05 |
| 11,497 | Towards Crowd-aware Indoor Path Planning | 2021 | VLDB | 4.1945683e-05 |
| 11,655 | Top-k Queries over Digital Traces | 2019 | SIGMOD | 4.1945683e-05 |
| 13,239 | IndoorViz: A Demonstration System for Indoor Spatial Data Management | 2021 | SIGMOD | - |
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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 |
|---|---|---|---|---|
| 2,149 | Spatial Keyword Query Processing: An Experimental Evaluation | 2013 | VLDB | 9.4266468e-05 |
| 6,200 | k-Nearest Neighbors on Road Networks: A Journey in Experimentation and In-Memory Implementation | 2016 | VLDB | 5.1602201e-05 |
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