iRoad: A Framework For Scalable Predictive Query Processing On Road Networks
Summary: iRoad supports scalable predictive point, range, KNN, and aggregate queries over moving objects on massive road networks. Its reachability tree prunes future-accessible regions, enabling efficient time-T prediction and interactive visualization under large workloads. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Abdeltawab M. Hendawi (University of Minnesota)
- 2. Jie Bao (University of Minnesota)
- 3. Mohamed F. Mokbel (University of Minnesota)
BibTeX Citation
@article{hendawi_vldb13,
title = {{iRoad: A Framework For Scalable Predictive Query Processing On Road Networks}},
author = {Hendawi, Abdeltawab M. and Bao, Jie and Mokbel, Mohamed F.},
journal = {PVLDB},
series = {{VLDB} '13},
volume = {6},
number = {12},
pages = {1262--1265},
doi = {10.14778/2536274.2536293},
url = {https://doi.org/10.14778/2536274.2536293},
year = {2013}
}
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,452 | Automatic Road Extraction with Multi-Source Data Revisited: Completeness, Smoothness and Discrimination | 2023 | VLDB | 5.093636e-05 |
| 13,543 | RxSpatial: Reactive Spatial Library for Real-Time Location Tracking and Processing | 2016 | SIGMOD | - |
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Outgoing Citations (Sorted by Pagerank)
Showing 1 of 1 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 449 | Query Processing in Spatial Network Databases | 2003 | VLDB | 0.0001826404 |
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