Effective Variation Management for Pseudo Periodical Streams
Summary: Proposes Pattern Growth Graph (PGG) for online variation management in pseudo-periodic streams, using wave-patterns to capture evolution and enable one-pass detection. Stores only differing segments for compression with reconstructable accuracy and noise discrimination, validated on large real datasets. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Lv-an Tang
- 2. Bin Cui
- 3. Hongyan Li
- 4. Gaoshan Miao
- 5. Dongqing Yang
- 6. Xinbiao Zhou
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,684 | Finding Semantics in Time Series | 2011 | SIGMOD | 8.3154143e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 12 of 12 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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