This paper reports our experiences on the Scalable Network Of Workstation (SNOW) project, which implements a novel methodology to support user-level process migration for traditio...
Methods for learning Bayesian networks can discover dependency structure between observed variables. Although these methods are useful in many applications, they run into computat...
Eran Segal, Dana Pe'er, Aviv Regev, Daphne Koller,...
Peer-to-Peer (P2P) technologies promise to provide efficient distribution, sharing and management of resources, such as storage, processing, routing and other sundry service capabi...
PVM and other distributed computing systems have enabled the use of networks of workstations for parallel computation, but their approach of treating all networks as collections o...
In recent years, there has been a growing interest in applying Bayesian networks and their extensions to reconstruct regulatory networks from gene expression data. Since the gene ...