Imminence Monitoring of Critical Events: A Representation Learning Approach

Summary: Imminence monitoring in heterogeneous data streams via representation learning. Learns probabilistic state-machine patterns over relational streams to predict event imminence, handling varied substreams and attributes; claims substantive gains over IL-Miner and LSTM baselines. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: hd87d7ef6f5afd290
Venue: SIGMOD
Year: 2021
Pagerank: 5.8415134e-05
Overall Rank: 6,234 | 58.09%
DOI: 10.1145/3448016.3452804

Incoming Non-self Citations Over Time

Authors

1. Yan Li (University of Massachusetts Lowell)
2. Tingjian Ge (University of Massachusetts Lowell)

BibTeX Citation

@inproceedings{li_sigmod21,
        title = {{Imminence Monitoring of Critical Events: A Representation Learning Approach}},
        author = {Li, Yan and Ge, Tingjian},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3452804},
        url = {https://dl.acm.org/doi/10.1145/3448016.3452804},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
4,534	SimpleTS: An Efficient and Universal Model Selection Framework for Time Series Forecasting	2023	VLDB	6.5561421e-05
11,105	DISCES: Systematic Discovery of Event Stream Queries	2025	SIGMOD	4.9793485e-05

Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
1,278	Streaming Pattern Discovery in Multiple Time-Series	2005	VLDB	0.00011231952
6,101	Complex Event Recognition in the Big Data Era	2017	VLDB	5.8868179e-05
6,677	IL-Miner: Instance-Level Discovery of Complex Event Patterns	2017	VLDB	5.711278e-05

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