Scientific Workflow Management Systems (SWfMS)  allow for scientists to specify workflows consisting of activities (i.e., program invocations) and their data dependencies. Scientific workflows can be executed using local computing resources and High Performance Computing (HPC) environments
such as computing clusters, grids and clouds. Although several SWfMS provide mechanisms for executing large-scale scientific workflows in distributed environments [4,8,9] most of them perform the workflow execution in an "offine" way, according to Ailamaki et al. . Existing approaches provide results and provenance data  that can only be analyzed after processing the entire dataset within
the workflow. However, as the experiment complexity, the volume of data and the need for computing power are on the rise, scientists need mechanisms for monitoring, analyzing partial results, and taking action during workflow execution.
CFD analyses, for example, take several factors into consideration: geometry, viscosity, mesh partitioning, time step size, wall time and the frequency at which the results are stored; just to name a
few. According to the initial setup, the simulation may produce huge amounts of data. Based on the produced outcome, scientists may see they need to explore the simulation diFFerently. They may need to refine the mesh, change time step size or store more or less results during specific simulation time intervals. Nowadays, scientists simply run the simulation again from the beginning. However, if they
have a significant feedback of what is currently happening, they can take actions during the execution and profit from a better resource utilization. Besides, steering the execution of a workflow may help scientists to achieve the desired outcome faster.
In this work, we discuss algorithms and techniques that may give scientists the possibility to steer
their experiments taking advantage of querying provenance data at real-time. When scientists run their workflows, provenance records keep track of everything that has already happened, what is currently happening and what still needs to be executed in the workflow. Thus we present our ongoing approaches to handle what we believe are the three main issues related to steering in scientific
workflows: (i) monitoring of execution, (ii) data analysis at runtime, and (iii) dynamic interference in the execution.
For monitoring and notification, SciLightning  noties scientists about events that are important through mobile devices and social networks (e.g., Facebook, SMS, and Twitter) and opens a communication channel between the mobile device and the remote (e.g. cloud) execution. For data analysis,
Prov-Viz  allows for querying and traversing the provenance database, to stage out selected data and visualize them in a local machine or on tiled wall displays. We also show our ongoing approach to interfere in the execution using a provenance API for steering Chiron , our algebraic workflow engine.
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