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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CONTENTS 1 Contents 1 Executive Summary 2 2 Background 4 3 Scientific Workflows 4 3.1 Example Workflows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 3.1.1 Promoter Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 3.1.2 Mineral Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.1.3 Job Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.2 Requirements and Desiderata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 3.3 Di#erences to Business Workflows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 4 SPA Technology Development 10 4.1 Web Service Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 4.2 Grid and other Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 4.3 Actor-Oriented Modeling . . . . . . .

Some cyclic block diagrams need parallel semantics: they are syntactically invalid or semantically non-terminating in any block diagram language with sequential semantics. Yet, many cyclic block diagrams do not need parallel semantics: they behave the same in a block diagram language with nonstrict sequential semantics. We show that a block diagram used to motivate the parallel semantics of the Ptolemy SR domain behaves the same in BdHas, a block diagram language with nonstrict sequential semantics. BdHas is implemented as syntactic sugar on top of Haskell.

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We view scientific workflows as the domain scientist’s way to harness cyberinfrastructure for e-Science. Domain scientists are often interested in “end-to-end” frameworks which include data acquisition, transformation, analysis, visualization, and other steps. While there is no lack of technologies and standards to choose from, a simple, unified framework combining data modeling and processoriented modeling and design of scientific workflows has yet to emerge. Towards this end, we introduce a number of concepts such as models of computation and provenance, actor-oriented modeling, adapters, hybrid types, and higher-order components, and then outline a particular composition of some of these concepts, yielding a promising new synthesis for describing scientific workflows, i.e., Collection-Oriented Modeling and Design (COMAD).