Analysis of Grid Service Composition with BPEL4WS
AINA '04 Proceedings of the 18th International Conference on Advanced Information Networking and Applications - Volume 2
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Evaluation of BPEL to Scientific Workflows
CCGRID '06 Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid
WS Binder: a framework to enable dynamic binding of composite web services
Proceedings of the 2006 international workshop on Service-oriented software engineering
Choreography for the Grid: towards fitting BPEL to the resource framework: Research Articles
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Programming scientific and distributed workflow with Triana services: Research Articles
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Pegasus: A framework for mapping complex scientific workflows onto distributed systems
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Grid-Enabled Workflow Management System Based On BPEL
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Cooperative E-Organizations for Distributed Bioinformatics Experiments
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Workflows and e-Science: An overview of workflow system features and capabilities
Future Generation Computer Systems
Scientific workflow design for mere mortals
Future Generation Computer Systems
Future Generation Computer Systems
On-Demand Resource Provisioning for BPEL Workflows Using Amazon's Elastic Compute Cloud
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
A Framework for Multi-class Learning in Micro-array Data Analysis
AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
A grid portal for solving geoscience problems using distributed knowledge discovery services
Future Generation Computer Systems
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ACM SIGKDD Explorations Newsletter
A light-weight grid workflow execution engine enabling client and middleware independence
PPAM'07 Proceedings of the 7th international conference on Parallel processing and applied mathematics
A service-based approach for the execution of scientific workflows in grids
Proceedings of the 7th ACM international conference on Computing frontiers
Grid-enabled Spatial Data Infrastructure for environmental sciences: Challenges and opportunities
Future Generation Computer Systems
WPS orchestration using the Taverna workbench: The eScience approach
Computers & Geosciences
An SOA-based model for the integrated provisioning of cloud and grid resources
Advances in Software Engineering
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In the business world, Service Oriented Architecture (SOA) has recently gained popularity as a new approach to integrate business applications. In a similar way, scientific workflows can accomplish the automation of large-scale e-Science applications. However, the use of workflows in scientific environments differs from that in business environments. Scientific workflows need to handle large amounts of data, deal with heterogeneous and distributed resources such as the Grid, and require specialized software applications that are written in diverse programming languages, most of which are not popular in business environments. In this paper, we analyze the preparedness and the shortcomings of the SOA paradigm in addressing the needs of e-Science and the extent to which this can be done. The paper identifies the characteristics of a Virtual Organization providing scientific services, and presents a model placing particular emphasis on BPEL processes as a mean for supporting the interaction with Web Services. We discuss the challenges encountered in the seamless integration of BPEL processes within an e-Science infrastructure and we propose a set of complementary infrastructural services. By providing business utilities and automation technology founded on Web Services, infrastructural services cooperate with BPEL in ensuring on-demand resource provisioning for the execution of scientific workflows, while addressing some critical issues such as security, access control and monitoring. Furthermore, the paper presents our experience in adopting the proposed approach within a collaborative environment for bioinformatics. To illustrate how a scientific experiment can be formalized and executed, we focus on micro-array data processing, a field that will be increasingly common in applications of machine learning to molecular biology.