Most of modern enterprises, institutions, and organizations rely
on knowledge-based management systems. In these systems, knowledge
is gained from data analysis. Nowadays, knowledge-based management
systems include data warehouses as their core components. The
purpose of building a data warehouse is twofold. Firstly, to
integrate multiple heterogeneous, autonomous, and distributed data
sources within an enterprise. Secondly, to provide a platform for
advanced, complex, and efficient data analysis. Data integrated in
a data warehouse are analyzed by the so-called On-Line Analytical
Processing (OLAP) applications designed among others for
discovering trends, patterns of behavior, and anomalies as well as
for finding dependencies between data. Massive amounts of
integrated data and the complexity of integrated data that more and
more often come from WEB-based, XML-based, spatio-temporal, object,
and multimedia systems, make data integration and processing
challenging.
The objective of NEW TRENDS IN DATA WAREHOUSING AND DATA
ANALYSIS is fourfold: First, to bring together the most recent
research and practical achievements in the DW and OLAP
technologies. Second, to open and discuss new, just emerging areas
of further development. Third, to provide the up-to-date
bibliography of published works and the resource of research
achievements for anyone interested in up-to-date data warehouse
issues. And, finally, to assist in the dissemination of knowledge
in the field of advanced DW and OLAP.
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