  The traditional lower bound estimation method for powerlaw distributions
based on the Kolmogorov-Smirnov distance proved to perform better than other
competing methods. However, if applied to very large collections of data, such
a method can be computationally demanding. In this paper, we propose two
alternative methods with the aim to reduce the time required by the estimation
procedure. We apply the traditional method and the two proposed methods to
large collections of data ($N = 500,000$) with varying values of the true lower
bound. Both the proposed methods yield a significantly better performance and
accuracy than the traditional method.
