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STEM: A Spatio-TEmporal Miner for Bursty Activity

Summary: STEM mines spatiotemporal burstiness by jointly extracting bursty time windows and the streams showing them in geo-stamped data. Supports diverse data sources (news, microblogs), end-to-end mining, and a tidy interface for pattern specification. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
h98c092f26a3bfc8e
Venue
SIGMOD
Year
2013
Pagerank
4.9793485e-05
Overall Rank
12,526 | 15.79%
DOI
10.1145/2463676.2463688

Incoming Non-self Citations Over Time

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Authors

BibTeX Citation

@inproceedings{lappas_sigmod13,
        title = {{STEM: A Spatio-TEmporal Miner for Bursty Activity}},
        author = {Lappas, Theodoros and Vieira, Marcos R. and Gunopulos, Dimitrios and Tsotras, Vassilis J.},
        series = {{SIGMOD} '13},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/2463676.2463688},
        url = {https://dl.acm.org/doi/10.1145/2463676.2463688},
        year = {2013}
}

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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.

Rank Cited Paper Year Venue Pagerank
2,683 Identifying Similarities, Periodicities and Bursts for Online Search Queries 2004 SIGMOD 8.1393112e-05
2,760 BlogScope: A System for Online Analysis of High Volume Text Streams 2007 VLDB 8.0493804e-05
3,939 On the Spatiotemporal Burstiness of Terms 2012 VLDB 6.9147782e-05
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