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- W2475386006 abstract "The life-science and health care research environments offer an abundance of new opportunities for improvement of their efficiency and productivity using big data in collaborative research processes. A key component of this development is e-Science analytics, which is typically supported by Cloud computing nowadays. However, the state-of-the-art Cloud technology does not provide an appropriate support for high-productivity e-Science analytics. In this paper, we show how productivity of Cloud-based analytics systems can be increased by (a) supporting researchers with integrating multiple problem solving environments into the life cycle of data analysis, (b) parallel code execution on top of multiple cores or computing machines, (c) enabling safe inclusion of sensitive datasets into analytical processes through improved security mechanisms, (d) introducing scientific dataspace-a novel data management abstraction, and (e) automatic analysis services enabling a faster discovery of scientific insights and providing hints to detect potential new topics of interests. Moreover, an appropriate formal productivity model for evaluating infrastructure design decisions was developed. The result of the realization of this vision, a key contribution of this effort, is called the High-Productivity Framework that was tested and evaluated using real life-science application domain addressing breath gas analysis applied e.g. in the cancer treatment." @default.
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- W2475386006 date "2016-05-01" @default.
- W2475386006 modified "2023-09-23" @default.
- W2475386006 title "Towards a novel infrastructure for conducting high productive cloud-based scientific analytics" @default.
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- W2475386006 doi "https://doi.org/10.1109/mipro.2016.7522135" @default.
- W2475386006 hasPublicationYear "2016" @default.
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