Statistical results of parallel revevant backward inference

The relevant backward inference, based on the solution of logical equations, is an effective tool for research and optimization of production-logical systems. It can significantly reduce the number of slow-running queries to an external source of information. Previous papers describe the implementation of a multi-threaded algorithm for relevant backward inference. It is based on parallelizing steps in constructing a set of facts that are required in the inference.

This article describes results of the statistical data research, obtained by performing a number of tests with using the relevant backward inference and its parallel implementation. It presents illustrations, charts and graphs, generated by data processing in the Statistica.

The results for multi-threading implementation show a 20-25% average reduction of time required to find a solution. These figures are considered to be statistical indicators, as the classical backward inference allows to obtain the result immediately in some cases.