  During the last decade or so, we have had a deluge of data from not only
science fields but also industry and commerce fields. Although the amount of
data available to us is constantly increasing, our ability to process it
becomes more and more difficult. Efficient discovery of useful knowledge from
these datasets is therefore becoming a challenge and a massive economic need.
This led to the need of developing large-scale data mining (DM) techniques to
deal with these huge datasets either from science or economic applications. In
this chapter, we present a new DDM system combining dataset-driven and
architecture-driven strategies. Data-driven strategies will consider the size
and heterogeneity of the data, while architecture driven will focus on the
distribution of the datasets. This system is based on a Grid middleware tools
that integrate appropriate large data manipulation operations. Therefore, this
allows more dynamicity and autonomicity during the mining, integrating and
processing phases
