Orca-SR: A Real-Time Traffic Engineering Framework leveraging Similarity Joins
Summary: Orca-SR is a real-time traffic‑engineering framework for SRP-based signal reconstruction that leverages similarity joins. The web demo scales to large networks, enabling interactive generation/loading of traffic flows to study how traffic patterns impact reconstruction quality in real time. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Jees Augustine (University of Texas)
- 2. Suraj Shetiya (University of Texas)
- 3. Abolfazl Asudeh (University of Illinois Chicago)
- 4. Saravanan Thirumuruganathan (Hamad Bin Khalifa University; Qatar Computing Research Institute)
- 5. Azade Nazi (Google)
- 6. Nan Zhang (American University)
- 7. Gautam Das (University of Texas)
- 8. Divesh Srivastava (AT&T)
BibTeX Citation
@article{augustine_vldb20,
title = {{Orca-SR: A Real-Time Traffic Engineering Framework leveraging Similarity Joins}},
author = {Augustine, Jees and Shetiya, Suraj and Asudeh, Abolfazl and Thirumuruganathan, Saravanan and Nazi, Azade and Zhang, Nan and Das, Gautam and Srivastava, Divesh},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {12},
pages = {2977--2980},
doi = {10.14778/3415478.3415523},
url = {https://doi.org/10.14778/3415478.3415523},
year = {2020}
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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 |
|---|---|---|---|---|
| 2,308 | Hashed Samples: Selectivity Estimators For Set Similarity Selection Queries | 2008 | VLDB | 8.7738996e-05 |
| 9,076 | Leveraging Similarity Joins for Signal Reconstruction | 2018 | VLDB | 5.3251649e-05 |
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