Neural network architecture for high-speed database query processing

Artificial neural networks (ANNs), due to their inherent parallelism and potential fault-tolerance, offer an attractive paradigm for robust and efficient implementations of large modern database and knowledge base systems. This paper explores a neural network model for efficient implementation of a database query system. The application of the proposed model to a high-speed library query system for retrieval of multiple items is based on the partial match of the specified query criteria with the stored records. The performance of the ANN realization of the database query module is analyzed and compared with other techniques commonly found in current computer systems. The results of this analysis suggest that the proposed ANN design offers an attractive approach for the realization of query modules in large database and knowledge base systems, especially for retrievals based on partial matches.