Active and Accelerated Learning of Cost Models for Optimizing Scientific Applications

Summary: NIMO learns cost models for predicting execution times of scientific workflows on grids. It uses active, noninvasive sampling with passive instrumentation to train from few runs, cutting data needs and learning time. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 9468
Venue: VLDB
Year: 2006
Pagerank: 5.3009887e-05
Overall Rank: 5,850 | 59.31%
DOI: -

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
1,532	Data Management in Machine Learning: Challenges, Techniques, and Systems	2017	SIGMOD	0.00011472681
2,084	The Case for Predictive Database Systems: Opportunities and Challenges	2011	CIDR	9.5820534e-05
4,372	Automated and On-Demand Provisioning of Virtual Machines for Database Applications	2007	SIGMOD	6.2454934e-05
12,311	Large-Scale Uncertainty Management Systems: Learning and Exploiting Your Data (Tutorial Summary)	2009	SIGMOD	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

Showing 2 of 2 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
2,665	Statistical Learning Techniques for Costing XML Queries	2005	VLDB	8.3498101e-05
5,393	ZOO: A Desktop Experiment Management Environment	1997	SIGMOD	5.5326074e-05

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