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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)

Paper ID
10776
Venue
VLDB
Year
2013
Pagerank
5.4119882e-05
Overall Rank
8,539 | 41.42%
DOI
10.14778/2536274.2536293

Incoming Non-self Citations Over Time

Authors

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}
}

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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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