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
Detecting common scientific workflow fragments using templates and execution provenance
Proceedings of the seventh international conference on Knowledge capture
Large-scale multimedia content analysis using scientific workflows
Proceedings of the 21st ACM international conference on Multimedia
Time-bound analytic tasks on large datasets through dynamic configuration of workflows
WORKS '13 Proceedings of the 8th Workshop on Workflows in Support of Large-Scale Science
Structured analysis of the ISI Atomic Pair Actions dataset using workflows
Pattern Recognition Letters
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Data analytics involves choosing between many different algorithms and experimenting with possible combinations of those algorithms. Existing approaches however do not support scientists with the laborious tasks of exploring the design space of computational experiments. We have developed a framework to assist scientists with data analysis tasks in particular machine learning and data mining. It takes advantage of the unique capabilities of the Wings workflow system to reason about semantic constraints. We show how the framework can rule out invalid workflows and help scientists to explore the design space. We demonstrate our system in the domain of text analytics, and outline the benefits of our approach.