Abstract
Scientific computing has entered a new era of scale and sharing with the arrival of cyberinfrastructure facilities for computational experimentation. A key emerging concept is scientific workflows, which provide a declarative representation of complex scientific applications that can be automatically managed and executed in distributed shared resources. In the coming decades, computational experimentation will push the boundaries of current cyberinfrastructure in terms of inter-disciplinary scope and integrative models of scientific phenomena under study. This paper argues that knowledge-rich workflow environments will provide necessary capabilities for that vision by assisting scientists to validate and vet complex analysis processes and by automating important aspects of scientific exploration and discovery.
Highlights
Computational experimentation is a ubiquitous technique across science domains
The section introduces workflows, the capabilities that workflow systems are already contributing to scientific computing, and the benefits that result from using workflow environments in science projects
An example of a knowledge-level description would describe an autonomous vehicle in terms of its ability to pursue standing goals of going to a destination, to incorporate opportunistic goals when a lane opens, and to defend itself from other drivers through fast reactive behaviors. If we take this distinction to a workflow environment, we can see that the capabilities of workflow systems to map and execute workflows are concerned with the architecture at the symbol level
Summary
Computational experimentation is a ubiquitous technique across science domains It encompasses all aspects of the scientific experimentation process including data analysis, simulation, hypothesis generation and hypothesis testing. This paper argues that despite the clear impact of current cyberinfrastructure in science, there are severe limitations in terms of the breadth and scope that can be supported It introduces computational workflows as key artifacts to further computational science. Looking forward, the paper discusses the need to assist scientists at a higher level that requires capturing and exploiting scientific knowledge about the software and data used in computational experimentation It presents current research in workflow systems that exploit this knowledge to automate complex validations and decision making on behalf of the scientist. It presents five areas of future research where knowledge-rich workflow systems can provide significant added value to existing cyberinfrastructure capabilities for computational experimentation
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