Taverna: lessons in creating a workflow environment for the life sciences: Research Articles
Concurrency and Computation: Practice & Experience - Workflow in Grid Systems
Pegasus: A framework for mapping complex scientific workflows onto distributed systems
Scientific Programming
Google fusion tables: data management, integration and collaboration in the cloud
Proceedings of the 1st ACM symposium on Cloud computing
Google fusion tables: web-centered data management and collaboration
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Building Mashups by Demonstration
ACM Transactions on the Web (TWEB)
Journal of Experimental & Theoretical Artificial Intelligence
Large-scale multimedia content analysis using scientific workflows
Proceedings of the 21st ACM international conference on Multimedia
Diagnosing correctness of semantic workflow models
Data & Knowledge Engineering
Structured analysis of the ISI Atomic Pair Actions dataset using workflows
Pattern Recognition Letters
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Scientific metadata containing semantic descriptions of scientific data is expensive to capture and is typically not used across entire data analytic processes. We present an approach where semantic metadata is generated as scientific data is being prepared, and then subsequently used to configure models and to customize them to the data. The metadata captured includes sensor descriptions, data characteristics, data types, and process documentation. This metadata is then used in a workflow system to select analytic models dynamically and to set up model parameters automatically. In addition, all aspects of data processing are documented, and the system is able to generate extensive provenance records for new data products based on the metadata. As a result, the system can dynamically select analytic models based on the metadata properties of the data it is processing, generating more accurate results. We show results in analyzing stream metabolism for watershed ecosystem management.