Comprehensive, end-to-end, data and workflow management solutions are needed to handle the increasing complexity of processes and data volumes associated with modern distributed scientific problem solving, such as ultrascale simulations and high-throughput experiments. The key to the solution is an integrated network-based framework that is functional, dependable, faulttolerant, and supports data and process provenance. Such a framework needs to make development and use of application workflows dramatically easier so that scientists' efforts can shift away from data management and utility software development to scientific research and discovery. An integrated view of these activities is provided by the notion of scientific workflows - a series of structured activities and computations that arise in scientific problem-solving. An information technology framework that supports scientific workflows is the Ptolemy II based environment called Kepler. This paper discusses the issues associated with practical automation of scientific processes and workflows and illustrates this with workflows developed using the Kepler framework and tools.