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

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Actor-oriented components emphasize concurrency and temporal semantics and are used for modeling and designing embedded software and hardware. Actors interact with one another through ports via a messaging schema that can follow any of several concurrent semantics. Domainspecific actor-oriented languages and frameworks are common (Simulink, LabVIEW, SystemC, etc.). However, they lack many modularity and abstraction mechanisms that programmers have become accustomed to in object-oriented components, such as classes, inheritance, interfaces, and polymorphism, except as inherited from the host language. This paper shows a form that such mechanisms can take in actor-oriented components, gives a formal structure, and describes a prototype implementation. The mechanisms support actor-oriented class definitions, subclassing, inheritance, and overriding. The formal structure imposes structural constraints on a model (mainly the “derivation invariant”) that lead to a policy to govern inheritance. In particular, the structural constraints permit a disciplined form of multiple inheritance with unambiguous inheritance and overriding behavior. The policy is based formally on a generalized ultrametric space with some remarkable properties. In this space, inheritance is favored when actors are “closer” (in the generalized ultrametric), and we show that when inheritance can occur from multiple sources, one source is always unambiguously closer than the other.

Scientists are confronted with significant datamanagement problems due to the large volume and high complexity of scientific data. In particular, the latter makes data integration a difficult technical challenge. In this paper, we describe our work on semantic mediation and scientific workflows, and discuss how these technologies address integration challenges in scientific data management. We first give an overview of the main data-integration problems that arise from heterogeneity in the syntax, structure, and semantics of data. Starting from a traditional mediator approach, we show how semantic extensions can facilitate data integration in complex, multipleworlds scenarios, where data sources cover different but related scientific domains. Such scenarios are not amenable to conventional schema-integration approaches. The core idea of semantic mediation is to augment database mediators and query evaluation algorithms with appropriate knowledge-representation techniques to exploit information from shared ontologies. Semantic mediation relies on semantic data registration, which associates existing data with semantic information from an ontology. The Kepler scientific workflow system addresses the problem of synthesizing, from existing tools and applications, reusable workflow components and analytical pipelines to automate scientific analyses. After presenting core features and example workflows in Kepler, we present a framework for adding semantic information to scientific workflows. The resulting system is aware of semantically plausible connections between workflow components as well as between data sources and workflow components. This information can be used by the scientist during workflow design, and by the workflow engineer for creating data transformation steps ...

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Abstract — The platform-based design methodology [1] is based on the usage of formal modeling techniques, clearly defined abstraction levels and the separation of concerns to enable an effective design process. The METROPOLIS framework embodies the platform-based design methodology and has been applied to a number of case studies across multiple domains. Based on these experiences, we have identified three key features that need to be enhanced: heterogeneous IP import, orthogonalization of performance from behavior, and design space exploration. The next generation METRO II framework incorporates these advanced features. The main concepts underlying METRO II are described in this paper and illustrated with a small example. I.

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The Center for Plasma Edge Simulation (CPES) is a recently funded prototype Fusion Simulation Project, which is part of the DOE SciDAC program. Our center is developing a novel integrated predictive plasma edge simulation framework, which is applicable to existing magnetic fusion facilities (D3D, NSTX, CMOD) and next generation burning plasma experiments, e.g. ITER. The success of this project will be in developing and understanding new models for the plasma edge in a kinetic regime with complex geometry. Because of the multi-scale nature of the problem, we will study the neoclassical physics time scale kinetically, and the fast and larger scale MHD modes via a fluid code. Our approach is to couple these codes via a scientific workflow system, Kepler-HPC. Kepler-HPC will enhance Kepler with capabilities such as code coupling and data redistribution, high volume data transfers and interactive (and autonomic) monitoring, steering and debugging, which will be necessary for scientific progress in this project. 1.