![]() ![]() Abstract The ever-increasing size of neuroimaging data repositories around the world and the development of numerous algorithmic toolkits for their analysis have clearly accelerated and enhanced neuroimaging based research. ![]() UWE accepts no liability for any infringement of intellectual property rights in any material deposited but will remove such material from public view pend-ing investigation in the event of an allegation of any such infringement. UWE makes no representation that the use of the materials will not infringe any patent, copyright, trademark or other property or proprietary rights. UWE makes no representation or warranties of commercial utility, title, or fit-ness for a particular purpose or any other warranty, express or implied in respect of any material deposited. We illustrate our approach with a real-world workflow for maxillo facial surgery simulation.ĭisclaimer UWE has obtained warranties from all depositors as to their title in the material deposited and as to their right to deposit such material. Workflow is specified graphically, in an intuitive manner, based on a standard vi-sual modeling language. Our QoS-aware work-flow system provides support for the whole workflow life cy-cle from specification to execution. For in-stance, for security or legal reasons the user may express the location affinity regarding Grid resources on which cer-tain workflow tasks may be executed. Besides performance related QoS, it includes economical, legal and security aspects. In this paper we present an approach for high level workflow specification that considers a comprehensive set of QoS requirements. However, most of the existing workflow languages lack constructs for QoS specification, or provide only limited QoS support. We con-sider that for the wide acceptance of Grid technology it is relevant that the user has the possibility to express require-ments on Quality of Service (QoS) at workflow specification time. This is a complex and demanding scientific process that illustrates the benefits of using a semantically rich executable language for defining the process, supporting automatic creation of process provenance metadata, assuring data reproducibility, and supporting analysis of the data's scientific soundness.Īt a high level of abstraction Grid applications are com-monly specified based on the workflow paradigm. We illustrate the information that comprises an analytic web for a scientific process that measures and analyzes the flux of water through a forested watershed. The work described here is similar to work often referred to as "scientific workflow", but emphasizes the need for semantically richer, more rigorously defined process definition languages, such as those that were first developed to define software engineering processes. Here, we present the concept of an analytic web, which defines the scientific processes employed and details the exact application of those processes in creating derived datasets. ![]() The scientific processes used to create such datasets must be clearly documented so that scientists can evaluate their soundness, reproduce the results, and build upon them in responsible and appropriate ways. With the availability of powerful computational and communication systems, scientists now readily access large, complicated derived datasets and build on those results to produce, through further processing, yet other derived datasets of interest to themselves and others. This is a complex and demanding scientific process that illustrates the benefits of using a semantically rich, executable language for defining processes and for supporting automatic creation of process provenance metadata. The work described here is similar to work often referred to as ?scientific workflow,? but emphasizes the need for a semantically rich, rigorously defined process definition language. With the availability of powerful computational and communication systems, scientists now readily access large, complicated derived datasets and build on those results to produce, through further processing, yet other derived datasets of interest.
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