Distributed data mining of probabilistic knowledge

We present a distributed approach to data mining of a knowledge representation scheme known as Bayesian belief networks which are capable of dealing with uncertain knowledge. We make use of a machine learning paradigm and a distributed asynchronous search technique to achieve the task of distributed knowledge discovery from data. Our approach boasts a number of features, including dynamic load balancing and fault tolerance. Empirical experiments have been conducted to illustrate its feasibility, solving large scale Bayesian network discovery problems with multiple workstations.