Study on effective detection method for specific data of large database

摘要

in the process of detecting specific data of large database, when the traditional detection method is utilized for detecting specific data, it is vulnerable for interference of mass information, which makes the specific data detection process time-consuming, and of low efficiency. For this, an effective detection method for specific data of large database is proposed based on improved TFIDF algorithm, the information entropy between the specific data features of large database and the information entropy within the features are viewed as the weighted factor for specific data detection, nonlinear mapping ability of neural network is adopted to achieve calculation of weights and fuzzification of TFIDF algorithm, thus solving the detection problem for specific data of large database. The experimental results show that, improved algorithm for effective detection of specific data in large databases, can effectively reduce time consumed for detection of specific data, ensure the detection quality of specific data to meet customer requirements.