Many scientific disciplines are now data and information driven, and new scientific knowledge is often gained by scientists putting together data analysis and knowledge discovery "pipelines". A related trend is that more and more scientific communities realize the benefits of sharing their data and computational services, and are thus contributing to a distributed data and computational community infrastructure (a.k.a. "the Grid"). However, this infrastructure is only a means to an end and scientists ideally should be bothered little with its existence. The goal is for scientists to focus on development and use of what we call scientific workflows. These are networks of analytical steps that may involve, e.g., database access and querying steps, data analysis and mining steps, and many other steps including computationally intensive jobs on high performance cluster computers. In this paper we describe characteristics of and requirements for scientific workflows as identified in a number of our application projects. We then elaborate on Kepler, a particular scientific workflow system, currently under development across a number of scientific data management projects. We describe some key features of Kepler and its underlying Ptolemy ii system, planned extensions, and areas of future research. Kepler is a communitydriven, open source project, and we always welcome related projects and new contributors to join. *

In the United States, natural gas-fired generators gained increasing popularity in recent years due to the low fuel cost and emission, as well as the proven large gas reserves. Consequently, the highly interdependency between the gas and electricity networks is needed to be considered in the system operation. To improve the overall system operation and optimize the energy flow, an interval optimization based coordinated operating strategy for the gaselectricity integrated energy system (IES) is proposed in this paper considering demand response and wind power uncertainty. In the proposed model, the gas and electricity infrastructures are modeled in details and their operation constraints are fully considered, wherein the nonlinear characteristics are modeled including pipeline gas flow and compressors. Then a demand response program is incorporated in the optimization model and its effects on the IES operation are investigated. Based on interval mathematics, wind power uncertainty is represented as interval numbers instead of probability distributions. A case study is performed on a six-bus electricity network with a seven-node gas network to demonstrate the

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