MapReduce-based Bayesian automatic text classifier used in digital library

Abstract

Bayesian theorem is an effective method for text classification. But it will consume too much time and resource when used in large-scale database such as digital library. How to process data efficiently becomes a vital problem for the further development of digital library. Because Mapreduce model has strong capacity of processing mass data, Mapreduce-based Bayesian classifier can reduce the time that is caused by machine learning and therefore enhance the overall efficiency. The new method can classify documents into different groups according to its subject. In this way, it is helpful for management and storage of information. Thus Mapreduce-based Bayesian text classifier can be used in digital library successfully which will provide better service for people. © 2012 Springer-Verlag.