Distributed and Parallel Databases
Software Reuse Research: Status and Future
IEEE Transactions on Software Engineering
WOODSS and the Web: annotating and reusing scientific workflows
ACM SIGMOD Record
A taxonomy of scientific workflow systems for grid computing
ACM SIGMOD Record
Enabling ScientificWorkflow Reuse through Structured Composition of Dataflow and Control-Flow
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
Scientific workflow management and the Kepler system: Research Articles
Concurrency and Computation: Practice & Experience - Workflow in Grid Systems
Scientific workflow design for mere mortals
Future Generation Computer Systems
Future Generation Computer Systems
A data-driven workflow language for grids based on array programming principles
Proceedings of the 4th Workshop on Workflows in Support of Large-Scale Science
Wings: Intelligent Workflow-Based Design of Computational Experiments
IEEE Intelligent Systems
Seven bottlenecks to workflow reuse and repurposing
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
A framework for the design and reuse of grid workflows
SAG'04 Proceedings of the First international conference on Scientific Applications of Grid Computing
Computer-Assisted Scientific Workflow Design
Journal of Grid Computing
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An increasing number of scientific experiments are "in-silico": carried out at least partially using computers. Scientific Workflows have become a key tool to model and implement such experiments, but they tangle domain knowledge, technical know-how and non-functional concerns and are, as a result, difficult to understand, reuse or repurpose. In order to ease Scientific Workflow Reuse, this paper defines a Conceptual Workflow model that is closer to the end-user's domain and intentions. By placing our model higher on the abstraction scale, we can separate concerns and emphasize the in-silico experiment inside the workflow, thus improving readability and re-usability. The conceptual representation can then be transformed into a regular Abstract Scientific Workflow, exploiting both domain and non-functional knowledge that are captured and harnessed through the use of Semantic Web technologies.