A Demonstration of ST-Hadoop: A MapReduce Framework for Big Spatio-temporal Data
Summary: ST-Hadoop is the first open-source MapReduce framework with native spatio-temporal support, indexing ST data in HDFS to yield orders-of-magnitude speedups over Hadoop and SpatialHadoop for large queries. Demonstrated on a 24-node cluster with ~1B Twitter and NYC Taxi records. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,021 | VRE: A Versatile, Robust, and Economical Trajectory Data System | 2022 | VLDB | 4.8581131e-05 |
| 10,757 | Polaris: An Interactive and Scalable Data Infrastructure for Polar Science | 2025 | VLDB | 4.1945683e-05 |
| 11,188 | ST4ML: Machine Learning Oriented Spatio-Temporal Data Processing at Scale | 2023 | SIGMOD | 4.1945683e-05 |
| 11,606 | IMO: A Toolbox for Simulating and Querying "Infected" Moving Objects | 2020 | VLDB | 4.1945683e-05 |
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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