@article{article_116698, title={Random Neural Network Approach in Distributed Database Management Systems}, journal={IU-Journal of Electrical & Electronics Engineering}, volume={1}, pages={84–110}, year={2011}, author={Karahoca, Adem and Ucan, Osman N. and Danacı, Erkan}, abstract={<div>In this paper, Random Neural Network (RNN) approach has been applied to the distributed database </div> <div>design of technology-corridor prototype project for Avcýlar Campus of Istanbul University in Turkey. </div> <div>This project includes university, industry and government collaboration. Here, we need a distributed </div> <div>environment for designing sub databases and fragmenting them on the sites. Therefore, different </div> <div>techniques are considered for a database fragmentation. When techniques are described, eight different </div> <div>properties are controlled for database process behaviors. Fragmentation techniques are ordered for </div> <div>each property. These orders help us to make decision about which fragmentation technique is the best </div> <div>for distributed database system. Here RNN approach and Radial basis functions networks are used for </div> <div>generalization of selection of partitioning techniques. Training data of Radial basis function networks and </div> <div>RNN are provided from the programs, which are executing under Oracle database. In this paper, firstly </div> <div>we used Neural Networks approaches at distributed environments for automatic database fragmentation </div> <div>selection operation and designed two non-linear algorithms. Then, Random Neural Network Methods </div> <div>have been applied to the same problem and obtained satisfactory results. </div> <div>Key Words: Database, database design, distributed database, database fragmentation, neural </div> <div>networks, radial basis function networks, random neural network </div>}, number={1}, publisher={İstanbul University-Cerrahpasa}