An Algorithmic Approach to Event Summarization
Summary: Algorithmic event summarization via Hidden Markov Models to capture internal system dynamics and state transitions. Learned HMMs yield shorter description length and higher interpretability than piecewise summaries; experiments show efficiency and effectiveness. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Peng Wang
- 2. Haixun Wang
- 3. Majin Liu
- 4. Wei Wang
Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,680 | Finding Semantics in Time Series | 2011 | SIGMOD | 8.3234371e-05 |
| 9,057 | Behavior Query Discovery in System-Generated Temporal Graphs | 2016 | VLDB | 4.4039656e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 65 | Fast Subsequence Matching in Time-Series Databases | 1994 | SIGMOD | 0.00062029383 |
| 243 | Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases | 2001 | SIGMOD | 0.00031074984 |
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