We present an ensemble learning approach that achieves accurate predictions from arbitrarily partitioned data. The partitions come from the distributed processing requirements of ...
Larry Shoemaker, Robert E. Banfield, Lawrence O. H...
Computational grids hold great promise in utilizing geographically separated heterogeneous resources to solve large-scale complex scientific problems. However, a number of major ...
Peta-scale scientific applications running on High End Computing (HEC) platforms can generate large volumes of data. For high performance storage and in order to be useful to scien...
Fang Zheng, Hasan Abbasi, Ciprian Docan, Jay F. Lo...
Multilevel design problems are typically decomposed into a hierarchy of distributed and strongly coupled sub-problems, each solved by design teams with specialized knowledge and t...
Abstract. Grid computing is based on coordinated resource sharing in a dynamic environment of multi-institutional virtual organizations. Data exchanges, and service allocation, are...