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While many workflow languages, execution engines, and analysis and optimization techniques have been developed, the research so far has neglected the quality of data (QoD) aspects in the design, execution and analysis of workflows. Until now, QoD for input data, intermediate results and final results of workflows has not been dealt and has been assumed under the responsibility of the users. However, this assumption cannot be hold and the users cannot guarantee the QoD in their workflows due to the complexity and scale of input data and computational models and algorithms handling the input data and intermediate results in their workflows. We need novel QoD-aware techniques integrated into the workflow design and execution, otherwise, a lot of resources, energy and time, will be wasted for the design and execution of workflows that produce unacceptable quality of results.
Principal Investigators
External Collaborators, PhD students, Internship
Contact Hong-Linh Truong for further information.