The Use of a Modified Backpropagation Neural Network for Random Access to Data Files on Secondary Storage

Jim Etheredge

For many applications random access to data is critical to providing users with the level of efficiency necessary to make applications usable. It is also common to maintain data files in sequential order to allow batch processing of the data. This paper presents a method that uses a modified backpropagation neural network to locate records in a file randomly. The modifications necessary to the backpropagation model are presented. Correlations are drawn between the features of the backpropagation model and the access patterns common to data files. Finally, the performance of the neural network is compared to the B + tree indexing method commonly used to provide both sequential and random access to stored data files. The results presented show that the proposed method can provide performance comparable to the B + tree depending on the attributes of the file.

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