Abstract
Currently the computer is essential to the success in conducting scientific research. In this context, e-Science appears as science performed with computer support aiming efficiency. The challenge, “Computational Modeling of artificial, naturals and socio-cultural complex systems and man-nature interaction” from SBC Great Challenges is strongly related to the e-Science context. The goal of this challenge is to create, evaluate, modify, compose, manage and exploit computer models in fields related to complex, artificial, natural, socio-cultural and human-nature systems. Technologies like semantic web service composition, data provenance, peer to peer networks and scientific software product line can be used as basis for the specification and development of an e-Science infrastructure to handle challenges and solve problems. This paper discusses the main challenges involved in developing an eScience infrastructure, presenting research challenges for the next years.
Highlights
The term Computational Science, created to contrast with Computer Science, has been used to designate models, algorithms and computational tools as a solution of complex systems of different natures [Roure et al, 2003]
Computational resources are becoming increasingly important in the life cycle of a scientific research
This paper aimed to discuss the development of scientific research in a distributed context
Summary
The term Computational Science, created to contrast with Computer Science, has been used to designate models, algorithms and computational tools as a solution of complex systems of different natures [Roure et al, 2003] Through these applications scientists, from other knowledge domains can investigate problems that could not recently be considered because of the high volume of data, the absence of analytic solutions, or the impracticability of studying them in laboratories. There are several problems related to e-Science support Technologies such as peer-to-peer network, ontologies, composition and orchestration languages for semantic services, software product lines and data provenance management can be used as a base for the construction of an infrastructure to support e-Science.
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