Vítor Silva Sousa

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Scientific workflows are commonly used to model and execute large-scale scientific experiments. They represent key resources for scientists and are enacted and managed by Scientific Workflow Management Systems (SWfMS). Each SWfMS has its particular approach to execute workflows and to capture and manage their provenance data. Due to the large scale of(More)
In 2006 a group of leading researchers was gathered to discuss several challenges to scientific workflow supporting technologies and many of which still remain open challenges, such as the steering of workflows by users. Due to big data and long lasting workflows, many users demand steering features such as real-time monitoring, analysis and specially(More)
Scientific experiments based on computer simulations can be defined, executed and monitored using Scientific Workflow Management Systems (SWfMS). Several SWfMS are available, each with a different goal and a different engine. Due to the exploratory analysis, scientists need to run parameter sweep (PS) workflows, which are workflows that are invoked(More)
Scientific experiments present several advantages when modeled at high abstraction levels, independent from Scientific Workflow Management System (SWfMS) specification languages. For example, the scientist can define the scientific hypothesis in terms of algorithms and methods. Then, this high level experiment can be mapped into different scientific(More)
Striking evidence associates cancer stem cells (CSCs) to the high recurrence rates and poor survival of patients with muscle-invasive bladder cancer (BC). However, the prognostic implication of those cells in risk stratification is not firmly established, mainly due to the functional and phenotypic heterogeneity of CSCs populations, as well as, to the(More)
1 Scientific Workflow Management Systems manage experiments in large-scale and deliver provenance data. Provenance data represents the workflow execution behavior, allowing for tracing the data-flow generation. When provenance is extended with performance execution data, it becomes an important asset to identify and analyze errors that occurred during the(More)
Scientific applications generate raw data files in very large scale. Most of these files follow a standard format established by the domain area application, like HDF5, Net CDF and FITS. These formats are supported by a variety of programming languages, libraries and programs. Since they are in large scale, analyzing these files require writing a specific(More)