Similarity determination based on data types in heterogeneous databases using neural networks
Baohua Qiang, Kaigui Wu, Xiaofeng Liao, Zhongfu Wu · 2003
An important task of similarity determination in heterogeneous databases is to determine which fields refer to the same data. Neural networks have emerged as a powerful pattern recognition technique. But the most concerned problem of using neural networks is the training performance. It is not easy to get high performance. In this paper we present a new approach, similarity determination based on data types in heterogeneous databases using neural networks, to realize the concurrent computing of category learning and similarity determination. The experimental results show our approach can lower time complexity without reducing the precision ratio and recall ratio.