The Journal of Machine Learning Research
How Well Can Simple Metrics Represent the Performance of HPC Applications?
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
Proceedings of the 28th international conference on Software engineering
Future Generation Computer Systems
PLDA: Parallel Latent Dirichlet Allocation for Large-Scale Applications
AAIM '09 Proceedings of the 5th International Conference on Algorithmic Aspects in Information and Management
SMC-IT '09 Proceedings of the Third IEEE International Conference on Space Mission Challenges for Information Technology
Expressive Reusable Workflow Templates
E-SCIENCE '09 Proceedings of the 2009 Fifth IEEE International Conference on e-Science
Wings: Intelligent Workflow-Based Design of Computational Experiments
IEEE Intelligent Systems
Journal of Experimental & Theoretical Artificial Intelligence
A new approach for publishing workflows: abstractions, standards, and linked data
Proceedings of the 6th workshop on Workflows in support of large-scale science
Making data analysis expertise broadly accessible through workflows
Proceedings of the 6th workshop on Workflows in support of large-scale science
ESCIENCE '11 Proceedings of the 2011 IEEE Seventh International Conference on eScience
Tika in Action
Predicting the Execution Time of Workflow Activities Based on Their Input Features
SCC '12 Proceedings of the 2012 SC Companion: High Performance Computing, Networking Storage and Analysis
A General Approach to Real-Time Workflow Monitoring
SCC '12 Proceedings of the 2012 SC Companion: High Performance Computing, Networking Storage and Analysis
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Domain experts are often untrained in big data technologies and this limits their ability to exploit the data they have available. Workflow systems hide the complexities of high-end computing and software engineering by offering pre-packaged analytic steps combined into multi-step methods commonly used by experts. A current limitation of workflow systems is that they do not take into account user deadlines: they run workflows selected by the user, but take their time to do so. This is impractical when large datasets are at stake, since users often prefer to see an answer faster even if it has lower precision or quality. In this paper, we present an extension to workflow systems that enables them to take into account user deadlines by automatically generating alternative workflow candidates and ranking them according to performance estimates. The system makes these estimates based on workflow performance models created from workflow executions, and uses semantic technologies to reason about workflow options. Possible workflow candidates are presented to the user in a compact manner, and are ranked according to their runtime estimates. We have implemented this approach in the WOOT system, which combines and extends capabilities from the WINGS semantic workflow system and the Apache OODT Object Oriented Data Technology and workflow execution system.