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Knowledge Visualizer (KV) uses a General Logic Diagram (GLD) to display examples and/or various forms of knowledge learned from them in a planar model of a multi-dimensional discrete space. Knowledge can be in different forms, for example, decision rules, decision trees, logical expressions, clusters, classifiers, and neural nets with discrete input variables. KV is implemented as a module of the inductive database system VINLEN, which integrates a conventional database system with a range of inductive inference and data mining capabilities. This paper describes briefly the KV module and then focuses on the problem of arranging attributes that span the diagram in a way that leads to the most readable rule visualization in the diagram. This problem has been solved by applying a simulated annealing.

In this paper we describe a Grid-enabled system for monitoring HLA-based applications to enable load balancing by migration of federates. The monitoring is based on the OCM-G system [2]. We show how the design concepts of the OCM-G enable easy adaptation to monitoring of HLA, C++-based applications. The solution presented in this paper is transparent to the user application and does not require any changes to the original HLA RTI code. We also describe the role of monitoring the whole system for managing execution of HLA-based applications described in [25]. We discuss implementation issues and present test results for the monitoring overhead.