NILE-PDT: A Phenomenon Detection and Tracking Framework for Data Stream Management Systems
Summary: NILE-PDT detects and incrementally tracks phenomena—groups of streams with correlated behavior—by splitting candidate detection between Nile’s scalable stream operators and an application client. It adaptively steers query processing toward sensors influencing phenomenon propagation. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. M.H. Ali (Purdue University)
- 2. W.G. Aref (Purdue University)
- 3. R. Bose (University of Florida)
- 4. A.K. Elmagarmid (Purdue University)
- 5. A. Helal (University of Florida)
- 6. I. Kamel (Zayed University)
- 7. M.F. Mokbel (Purdue University)
BibTeX Citation
@article{ali_vldb05,
title = {{NILE-PDT: A Phenomenon Detection and Tracking Framework for Data Stream Management Systems}},
author = {Ali, M.H. and Aref, W.G. and Bose, R. and Elmagarmid, A.K. and Helal, A. and Kamel, I. and Mokbel, M.F.},
journal = {PVLDB},
series = {{VLDB} '05},
pages = {1295},
year = {2005}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,528 | State-Slice: New Paradigm of Multi-query Optimization of Window-based Stream Queries | 2006 | VLDB | 5.8502273e-05 |
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Outgoing Citations (Sorted by Pagerank)
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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|---|
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