  XML data sources are more and more gaining popularity in the context of a
wide family of Business Intelligence (BI) and On-Line Analytical Processing
(OLAP) applications, due to the amenities of XML in representing and managing
semi-structured and complex multidimensional data. As a consequence, many XML
data warehouse models have been proposed during past years in order to handle
hetero-geneity and complexity of multidimensional data in a way traditional
relational data warehouse approaches fail to achieve. However, XML-native
database systems currently suffer from limited performance, both in terms of
volumes of manageable data and query response time. Therefore , recent research
efforts are focusing the attention on fragmentation techniques, which are able
to overcome the limitations above. Derived horizontal fragmentation is already
used in relational data warehouses, and can definitely be adapted to the XML
context. However, classical fragmentation algorithms are not suitable to
control the number of originated fragments, which instead plays a critical role
in data warehouses, and, with more emphasis, distributed data warehouse
architectures. Inspired by this research challenge, in this paper we propose
the use of K-means clustering algorithm for effectively and efficiently
supporting the fragmentation of very large XML data warehouses, and, at the
same time, completely controlling and determining the number of originated
fragments via adequately setting the parameter K. We complete our analytical
contribution by means of a comprehensive experimental assessment where we
compare the efficiency of our proposed XML data warehouse fragmentation
technique against those of classical derived horizontal fragmentation
algorithms adapted to XML data warehouses.
