In the context of comparing two categorical maps, addressing the challenges posed by the dependence on marginal frequencies and spatial configuration is a formidable task. Map comparison typically relies on a numerical similarity index S, such as the spatial fuzzy kappa index, which has a probability distribution that varies according to the marginal frequencies of the classes and their spatial configuration in the maps. This variability makes it difficult to mathematically derive and assess the uncertainty associated with any empirical index. In this paper, we introduce two novel methods: the spatially thinned bootstrap and the random toroidal shift. These methods provide a robust framework for comparing two maps using an arbitrary similarity index while accounting for these marginal noise factors. Our approach is characterized by its generality and abstraction, enabling application across different indices of map similarity. To illustrate the effectiveness of our methods, we conduct an extensive study employing the spatial fuzzy kappa index, revealing valuable insights into the behavior and underlying distribution of similarity indices.

CORBA applications can transparently use service instances running on the client's machine, on the local-area network, or across the Internet. Standard CORBA services help the application locate service instances, but do not provide a mechanism to identify service instances that will give good performance. The PerformanceBroker executes performance test suites on application service instances and selects service instances that will give superior application performance. The Broker weights performance test results according to client-specified criteria to choose the service instances that will provide the best application performance and allocates those service instances to clients. Tests with a distributed ray tracing application show that service instances chosen by the PerformanceBroker give better performance than service instances chosen by round-robin or random selection in local-area network and Internet environments.