Effectively Indexing Uncertain Moving Objects for Predictive Queries
Summary: Combines probabilistic motion models with moving-object indexing. Efficient future-location inference from location/velocity distributions; integrates uncertainty into the Bx-tree, improving range and kNN queries in dynamic environments. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Meihui Zhang
- 2. Su Chen
- 3. Christian S. Jensen
- 4. Beng Chin Ooi
- 5. Zhenjie Zhang
Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,618 | Calibrating Trajectory Data for Similarity-based Analysis | 2013 | SIGMOD | 6.9085505e-05 |
| 9,256 | An Adaptive Updating Protocol for Reducing Moving Object Database Workload | 2010 | VLDB | 4.3690661e-05 |
| 9,494 | Spatial Data Quality in the IoT Era: Management and Exploitation | 2022 | SIGMOD | 4.3341665e-05 |
| 12,138 | MOIST: A Scalable and Parallel Moving Object Indexer with School Tracking | 2012 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 11 of 11 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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