High-Performance Knowledge Extraction from Data on PC-based Networks of Workstations

The automatic construction of classifiers (programs able to correctly classify data collected from the real world) is one of the major problems in pattern recognition and in a wide area related to Artificial Intelligence, including Data Mining. In this paper we present G-Net, a distributed algorithm able to infer classifiers from pre-collected data, and its implementation on PC-based Networks of Workstations (PC-NOWs). In order to effectively exploit the computing power provided by PC-NOWs, G-Net incorporates a set of dynamic load distribution techniques that allow it to adapt its behavior to variations in the computing power due to resource contention. Moreover, it is provided with a fault tolerance scheme that enables it to continue its computation even if the majority of the machines become unavailable during its execution