  Due to the distribution of linked data across the web, the methods that
process federated queries through a distributed approach are more attractive to
the users and have gained more prosperity. In distributed processing of
federated queries, we need methods and procedures to execute the query in an
optimal manner. Most of the existing methods perform the optimization task
based on some statistical information, whereas the query processor does not
have precise statistical information about their properties, since the data
sources are autonomous. When precise statistics are not available, the
possibility of wrong estimations will highly increase, and may lead to
inefficient execution of the query at runtime. Another problem of the existing
methods is that in the optimization phase, they assume that runtime conditions
of query execution are stable, while the environment in which federated queries
are executed over linked data is dynamic and non-predictable. By considering
these two problems, there is a great potential for exploiting the federated
query processing techniques in an adaptive manner. In this paper, an adaptive
method is proposed for processing federated queries over linked data, based on
the concept of routing the tuples. The proposed method, named ADQUEX, is able
to execute the query effectively without any prior statistical information.
This method can change the query execution plan at runtime so that less
intermediate results are produced. It can also adapt the execution plan to new
situation if unpredicted network latencies arise. Extensive evaluation of our
method by running real queries over well-known linked datasets shows very good
results especially for complex queries.
