A Performance Study of Two Inference Algorithms for a Distributed Expert System Shell

The rule knowledge-based systems are still popular in the real-world applications and the rules are considered as a standard form of knowledge representation in intelligent information systems. While the number of knowledge-based applications grows, the number of tools for building such systems grows much more slowly. This work is the part of research focused on the development of new methods and tools for building rule-based expert systems. The software components mentioned in this work are the main parts of the distributed expert system shell. The realized implementation assumes, that the inference is performed on the preloaded knowledge base stored in the memory. But such a way of using rule bases may be unrealisable or ineffective for large ones, especially when a weak hardware configuration (mobile applications, embedded systems) is used. In this work the utilization of a database stored procedures is considered. This approach minimizes the network traffic and is independent from the used programming tools—only a connection to the database server is required. The main goal of the experiments was to describe an experimental implementation of the forward chaining inference algorithm (as the stored procedure) and to evaluate this approach in comparison to performing inference on preloaded (real-world) knowledge bases.