  The recent information technology revolution has enabled the analysis and
processing of large-scale datasets describing human activities. The main source
of data is represented by the Web, where humans generally use to spend a
relevant part of their day. Here we study three large datasets containing the
information about Web human activities in different contexts. We study in
details inter-event and waiting time statistics. In both cases, the number of
subsequent operations which differ by tau units of time decays power-like as
tau increases. We use non-parametric statistical tests in order to estimate the
significance level of reliability of global distributions to describe activity
patterns of single users. Global inter-event time probability distributions are
not representative for the behavior of single users: the shape of single
users'inter-event distributions is strongly influenced by the total number of
operations performed by the users and distributions of the total number of
operations performed by users are heterogeneous. A universal behavior can be
anyway found by suppressing the intrinsic dependence of the global probability
distribution on the activity of the users. This suppression can be performed by
simply dividing the inter-event times with their average values. Differently,
waiting time probability distributions seem to be independent of the activity
of users and global probability distributions are able to significantly
represent the replying activity patterns of single users.
