Collaborative Knowledge Sharing for E-Science

Kirk D. Borne, Timothy Eastman

Science research programs have become massive data producers. This ability to produce large data volumes must be matched by technologies that make better use of the data flood and that facilitate reuse of the data, in order to reap the maximum scientific return from our research investments. In particular, the extraction and integration of knowledge from multiple data sources must become standard practice, both for science-enabled decision support and for scientific discovery. We describe the emerging e-Science paradigm and its application to data-driven knowledge discovery and collaborative knowledge sharing.

Subjects: 11. Knowledge Representation; 12. Machine Learning and Discovery


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