We present work in progress on a new methodology for leveraging the semantic content of ontologies to guide knowledge discovery in databases. Our system scans new databases to obtain type and constraint information, which users verify. Our system then uses this information in the context of a shared ontology to intelligently guide the potentially combinatorial process of feature construction. Further, our system learns each time it is applied, easing the user's verification task on subsequent runs..

Abstract We have studied the interaction between process‐based parallel programs whose characteristics change in various ways at run time and the operation of load‐balancing, as implemented by process migration. In order to do this, we propose a simple performance model, whose parameters represent features of the program's execution such as the frequency and regularity of the changes in computational characteristics, and conduct a series of experiments involving simulated executions of synthetic programs with controlled parameter values. From these we can deduce the relative importance of the parameters from the point of view of their influence on performance. We can explain our observations in terms of a simplified stochastic model that relates local changes in load to global behaviour. We show that the dynamics of load‐balancing can be represented approximately by a first‐order difference equation, and that the distributed process migration algorithm is consistent with a behaviour on the global scale which can be regarded as that of a traditional feedback controller.

We have developed a methodology for constructing performance models of different types of concurrent programs, and hence obtaining estimates of execution times on different multiprocessor machines. A given class of program is characterized in terms of a small set of parameters which summarise the behaviour of the program over time. Synthetic programs with selected sets of parameters can then be generated and their execution simulated on a model of some given parallel machine. By varying the parameters systematically, we can discover which factors most affect performance.