Anna Bánáti

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In the scientist's community one of the most vital challenges is the issue of reproducibility of workflow execution. In order to reproduce the results of an experiment, on one hand provenance information must be collected and on the other hand the dependencies of the execution need to be eliminated. Concerning the workflow execution environment we have(More)
The reproducibility of an in-silico experiment is a great challenge because of the parallel and distributed environment and the complexity of the scientific workflows. In order to solve such problems on one hand provenance data has to be captured about the dataflow, the ancestry of the results and the environment of the execution, on the other hand(More)
Scientific workflow systems aim to provide user friendly, end-to-end solutions for automating and simplifying computational or data intensive tasks. A number of workflow environments have been developed in recent years to provide support for the specification and execution of scientific workflows. Normal static workflows can poorly cope with the ever(More)
Applying scientific workflow to perform in-silico experiment is a more and more prevalent solution among the scientist's communities. Because of the data and compute intensive behavior of the scientific workflows parallel and distributed system (grids, clusters, clouds and supercomputers) are required to execute them. After all the complexity of these(More)
Smart system application are gaining significant attention especially concerning health monitoring researches. Data captured from sensors are arriving continuously and for an efficient handling of this large volume of data stream processing would give the ideal solution [6]. Stream processing means that without intermediate storage the data should be(More)
In almost all research field scientific studies can be implemented by in silico experiments. They are modelled by scientific workflows which describes the data or control flow between the consecutive computational tasks. Since these experiments are data and compute intensive they need parallel and distributed infrastructures to be enacted (grids, clusters,(More)
In the scientist's community one of the most vital challenges is the reproducibility of a workflow execution. The necessary parameters of the execution (we call them descriptors) can be external which depend on for example the computing infrastructure (grids, clusters and clouds), on third party resources or it can be internal which belong to the code of(More)
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