AI Summary & Key Takeaways
AI InsightThen, Random Neural Network Methods have been applied to the same problem and obtained satisfactory results.
📌 3 Key Takeaways
Abstract
In this paper, Random Neural Network (RNN) approach has been applied to the distributed database design of technology-corridor prototype project for Avcılar Campus of Istanbul University in Turkey. This project includes university, industry and government collaboration. Here, we need a distributed environment for designing sub databases and fragmenting them on the sites. Therefore, different techniques are considered for a database fragmentation. When techniques are described, eight different properties are controlled for database process behaviors. Fragmentation techniques are ordered for each property. These orders help us to make decision about which fragmentation technique is the best for distributed database system. Here RNN approach and Radial basis functions networks are used for generalization of selection of partitioning techniques. Training data of Radial basis function networks and RNN are provided from the programs, which are executing under Oracle database. In this paper, firstly we used Neural Networks approaches at distributed environments for automatic database fragmentation selection operation and designed two non-linear algorithms. Then, Random Neural Network Methods have been applied to the same problem and obtained satisfactory results.
Özet
In this paper, Random Neural Network (RNN) approach has been applied to the distributed database design of technology-corridor prototype project for Avcılar Campus of Istanbul University in Turkey. This project includes university, industry and government collaboration. Here, we need a distributed environment for designing sub databases and fragmenting them on the sites. Therefore, different techniques are considered for a database fragmentation. When techniques are described, eight different properties are controlled for database process behaviors. Fragmentation techniques are ordered for each property. These orders help us to make decision about which fragmentation technique is the best for distributed database system. Here RNN approach and Radial basis functions networks are used for generalization of selection of partitioning techniques. Training data of Radial basis function networks and RNN are provided from the programs, which are executing under Oracle database. In this paper, firstly we used Neural Networks approaches at distributed environments for automatic database fragmentation selection operation and designed two non-linear algorithms. Then, Random Neural Network Methods have been applied to the same problem and obtained satisfactory results.