Mining and Linking Patterns across Live Data Streams and Stream Archives
Summary: ViStreamT: visual analytics for interactive mining of patterns across streams and archives, fusing computation with visualization. Unique: real-time cross-stream extraction, multires, archival pushes, and evolution linking across time. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
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Authors
- 1. Di Yang
- 2. Kaiyu Zhao
- 3. Maryam Hasan
- 4. Hanyuan Lu
- 5. Elke Rundensteiner
- 6. Matthew Ward
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 662 | A Framework for Clustering Evolving Data Streams | 2003 | VLDB | 0.00018475968 |
| 7,419 | A Shared Execution Strategy for Multiple Pattern Mining Requests over Streaming Data | 2009 | VLDB | 4.7348504e-05 |
| 8,500 | Interactive Visual Exploration of Neighbor-Based Patterns in Data Streams | 2010 | SIGMOD | 4.4974096e-05 |
| 8,779 | Summarization and Matching of Density-Based Clusters in Streaming Environments | 2012 | VLDB | 4.4539178e-05 |
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