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Asking the Right Questions: Model-driven Optimization using Probes

Summary: Framework for model-driven selection of probes to resolve distributional uncertainty in parameters and maximize expected objectives under resource constraints, unifying tasks from anomaly detection to sensor networks and routing. Shows NP-hardness and gives greedy algorithms with provable approximation guarantees via novel submodularity arguments. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1392
Venue
PODS
Year
2006
Pagerank
4.1945683e-05
Overall Rank
12,488 | 13.13%
DOI
-

Incoming Non-self Citations Over Time

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
12,311 Large-Scale Uncertainty Management Systems: Learning and Exploiting Your Data (Tutorial Summary) 2009 SIGMOD 4.1945683e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 9 of 9 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
115 Eddies: Continuously Adaptive Query Processing 2000 SIGMOD 0.00046221215
477 Model-Driven Data Acquisition in Sensor Networks 2004 VLDB 0.00022221803
684 Towards a Robust Query Optimizer: A Principled and Practical Approach 2005 SIGMOD 0.00018179769
726 Load Shedding in a Data Stream Manager 2003 VLDB 0.00017511209
745 Distributed Top-K Monitoring 2003 SIGMOD 0.00017330487
1,236 Least Expected Cost Query Optimization: An Exercise in Utility 1999 PODS 0.00013117798
1,272 Proactive Re-Optimization 2005 SIGMOD 0.00012920076
2,180 Least Expected Cost Query Optimization: What Can We Expect? 2002 PODS 9.3481968e-05
3,801 On Computing Functions with Uncertainty 2001 PODS 6.7570059e-05
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