Refreshing materialized views is a main task of Web warehouse maintenance. As the refreshing scheme depends heavily on the base data change frequency, researchers have presented many corresponding algorithms and frequency estimators for it. Although these estimators really work, however, all of them have limitations. The bias that an estimator introduces will increase significantly when the estimated value is out of its applicable range. In this paper, a self-adaptive algorithm is presented based on Poisson process analysis, which can adjust the revisiting pattern and revisiting frequency according to the estimated change frequency. This algorithm can also tune the parameters so that the estimated value will fall into the best applicable range of the estimator. According to the experimental results, the proposed estimator is more accurate than the ones in the previous work.