e., data mining according to user??™s needs and perspectives). The implementation
of OLAP-like techniques relies on three operations on lattices, namely selection,
projection and assembly. A detailed running example serves to illustrate the scope
and benefits of the proposed techniques.
Introduction
Data mining (DM) is the process of discovering hidden knowledge (i.e., patterns and
associations) from large data sets while data warehousing (DW) aims at integrating
and aggregating data from multiple data sources for further analysis (Chaudhuri &
Dayal, 1997; Han & Kamber, 2000). The two technologies present some common
features such as (1) information/knowledge extraction from very large data sets,
(2) support for decision making, (3) use of background knowledge for additional
information (knowledge) extraction, and (4) need for a careful and generally timeconsuming
data preprocessing step.
There are many topics that have attracted researchers in the area of data warehousing:
data warehouse design and multidimensional modeling, efficient cube computation,
query optimization, discovery-driven exploration of cubes, data mining
in cubes, and so on. In order to avoid computing a whole data cube, many studies
have focused on iceberg cube calculation (Xin, Han, Li, & Wah, 2003), semantic
summarization of cubes (Lakshmanan, Pei, & Zhao, 2002), and approximation of
cube computation (Shanmugasundaram, Fayyad, & Bradley, 1999).
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