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Efficient Computation of Top-k Frequent Terms over Spatio-temporal Ranges

Summary: Top-k Frequent Spatio-Temporal Terms (kFST) queries over geotagged posts return exact keywords and frequencies within a spatiotemporal region. An R-tree augmented with top-k sorted term lists (STLs) balances index size and query speed, with theory-guided STL length and extensive real-data experiments against baselines on disk-resident data. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5470
Venue
SIGMOD
Year
2017
Pagerank
5.593012e-05
Overall Rank
7,576 | 48.03%
DOI
10.1145/3035918.3064032

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{ahmed_sigmod17,
        title = {{Efficient Computation of Top-k Frequent Terms over Spatio-temporal Ranges}},
        author = {Ahmed, Pritom and Hasan, Mahbub and Kashyap, Abhijith and Hristidis, Vagelis and Tsotras, Vassilis J.},
        series = {{SIGMOD} '17},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3035918.3064032},
        url = {https://dl.acm.org/doi/10.1145/3035918.3064032},
        year = {2017}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
11,676 Proportionality in Spatial Keyword Search 2021 SIGMOD 5.093636e-05
11,850 Top-k Queries over Digital Traces 2019 SIGMOD 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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