Query logs record the queries and the actions of the users of search engines, 
and as such they contain valuable information about the interests, 
the preferences, and the behavior of the users, as well as their implicit 
feedback to search-engine results. Mining the wealth of information available 
in the query logs has many important applications including query-log 
analysis, user profiling and personalization, advertising, query 
recommendation, and more. The query-flow graph is an outcome of query-log 
mining and, at the same time, a useful tool for it. We propose a methodology 
that builds such a graph by mining time and textual information as well as 
aggregating queries from different users. Using this approach we build a 
real-world query-flow graph from a large-scale query log, and we demonstrate 
its utility in concrete applications, namely, finding logical sessions, 
and query recommendation. We believe, however, that the usefulness of the 
query-flow graph goes beyond these two applications.