In this paper we study how to refresh a local copy 
of an autonomous data source to maintain the copy up-to-date.
As the size of the data grows, it becomes
more difficult to maintain the copy "fresh,"
making it crucial to synchronize the copy effectively.
We define two freshness metrics, change models of the 
underlying data, and synchronization policies.
We analytically study how effective the various policies are.
We also experimentally verify our analysis, based on data
collected from 270 web sites for more than 4 months,
and we show that our new policy improves the "freshness"
very significantly compared to current policies in use.