  In the ALICE experiment hundreds of users are analyzing big datasets on a
Grid system. High throughput and short turn-around times are achieved by a
centralized system called the LEGO trains. This system combines analysis from
different users in so-called analysis trains which are then executed within the
same Grid jobs thereby reducing the number of times the data needs to be read
from the storage systems. The centralized trains improve the performance, the
usability for users and the bookkeeping in comparison to single user analysis.
The train system builds upon the already existing ALICE tools, i.e. the
analysis framework as well as the Grid submission and monitoring
infrastructure. The entry point to the train system is a web interface which is
used to configure the analysis and the desired datasets as well as to test and
submit the train. Several measures have been implemented to reduce the time a
train needs to finish and to increase the CPU efficiency.
