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)
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Authors
- 1. Yan Li
- 2. Tingjian Ge
- 3. Cindy Chen
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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 |
|---|---|---|---|---|
| 571 | Efficient Pattern Matching over Event Streams | 2008 | SIGMOD | 0.00016414499 |
| 3,680 | Complex Event Detection at Wire Speed with FPGAs | 2010 | VLDB | 7.2680762e-05 |
| 8,206 | History is a mirror to the future: Best-effort approximate complex event matching with insufficient resources | 2017 | VLDB | 5.5355338e-05 |
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