Data Stream Event Prediction Based on Timing Knowledge and State Transitions

Summary: Dynamic knowledge-graph for data streams; uses ephemeral state nodes to encode stream state and predict timing. End-to-end translation-based embeddings for graph construction and prediction; delivers accuracy 0.7-1 and throughput 1k-60k tuples/s on a PC, suitable for edge deployment. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 12267
Venue: VLDB
Year: 2020
Pagerank: 5.093636e-05
Overall Rank: 11,795 | 19.08%
DOI: 10.14778/3401960.3401973

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

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

BibTeX Citation

@article{li_vldb20,
        title = {{Data Stream Event Prediction Based on Timing Knowledge and State Transitions}},
        author = {Li, Yan and Ge, Tingjian and Chen, Cindy},
        journal = {PVLDB},
        series = {{VLDB} '20},
        volume = {13},
        number = {10},
        pages = {1779--1792},
        doi = {10.14778/3401960.3401973},
        url = {https://doi.org/10.14778/3401960.3401973},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

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
565	Efficient Pattern Matching over Event Streams	2008	SIGMOD	0.00016445548
3,744	Complex Event Detection at Wire Speed with FPGAs	2010	VLDB	7.1576479e-05
8,337	History is a mirror to the future: Best-effort approximate complex event matching with insufficient resources	2017	VLDB	5.4511087e-05

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