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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
6086
Venue
SIGMOD
Year
2021
Pagerank
6.0394015e-05
Overall Rank
6,101 | 57.60%
DOI
10.1145/3448016.3452804

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
5,427 SimpleTS: An Efficient and Universal Model Selection Framework for Time Series Forecasting 2023 VLDB 6.2932384e-05
10,387 DISCES: Systematic Discovery of Event Stream Queries 2025 SIGMOD 5.1725247e-05
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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
1,231 Streaming Pattern Discovery in Multiple Time-Series 2005 VLDB 0.00011658901
5,897 Complex Event Recognition in the Big Data Era 2017 VLDB 6.1115848e-05
6,459 IL-Miner: Instance-Level Discovery of Complex Event Patterns 2017 VLDB 5.929815e-05
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